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Potential PhD projects

Two students involved in a robotics engineering competition

There are opportunities for talented researchers to join the School of Computer Science and Engineering, with projects in the following areas:

Embedded systems

Artificial intelligence

Supervisory team : Professor Claude Sammut 

Project summary : Our rescue robot has sensors that can create 3D representations of its surroundings. In a rescue, it's helpful for the incident commander to have a graphical visualisation of the data so that they can reconstruct the disaster site. The School of Computer Science and Engineering and the Centre for Health Informatics have a display facility (VISLAB) that permits users to visualise data in three dimensions using stereo projection onto a large 'wedge' screen. 

This project can be approached in two stages. In the first stage, the data from the robot are collected off-line and programs are written to create a 3D reconstruction of the robot's surroundings to be viewed in the visualisation laboratory. In the second stage, we have the robot transmit its sensor data to the VISLAB computers for display in real-time. 

This project requires a good knowledge of computer graphics and will also require the student to learn about sensors such as stereo cameras, laser range finders and other 3D imaging devices. Some knowledge of networking and compression techniques will be useful for the second stage of the project. 

A scholarship/stipend may be available. 

For more information contact:  Prof. Claude Sammut

Biomedical image computing

Supervisory team:  Dr Yang Song

Project summary:  Various types of microscopy images are widely used in biological research to aid our understanding of human biology. Cellular and molecular morphologies give lots of information about the underlying biological processes. The ability to identify and describe the morphological information quantitative, objectively and efficiently is critical. In this PhD project, we'll investigate various computer vision, machine learning (especially deep learning) and statistical analysis methodologies to develop automated morphology analysis methods for microscopy images.

More research topics in computer vision and biomedical imaging can be found  here .

A scholarship/stipend may be available.

For more information contact:  Dr Yang Song

Supervisor team:  Professor Erik Meijering and Dr John Lock

Project summary:  Biologists use multiparametric microscopy to study the effects of drugs on human cells. This generates multichannel image data sets that are too voluminous for humans to analyse by eye and require computer vision methods to automate the data interpretation. The goal of this PhD project is to develop, implement, and test advanced computer vision and deep learning methods for this purpose to help accelerate the challenging process of drug discovery for new cancer therapies. This project is in collaboration with the School of Medical Sciences (SoMS) and will utilise a new and world-leading cell image data set capturing the effects of 114,400 novel drugs on the biological responses (phenotypes) of >25 million single cells.

For more information contact:  [email protected][email protected]

Supervisor team:  Professor Erik Meijering and Professor Arcot Sowmya

Project summary:  Current commercial 3D ultrasound systems for medical imaging studies often do not provide the ability to record volumes large enough to visualise entire organs. The first goal of this PhD project is to develop novel computational methods for fast and accurate image registration to digitally reconstruct whole organs from multiple ultrasound volumes. The second goal is to develop computer vision and deep learning methods for automated volumetric image segmentation and downstream statistical analysis. This project will be in collaboration with researchers from the UNSW School of Women’s and Children’s Health to improve monitoring organ development during pregnancy to support clinical diagnostics.

For more information contact:  [email protected][email protected]

Supervisor team:  Prof. Arcot Sowmya, A/Prof. Lois Holloway (Ingham Medical Research Institute, Liverpool Hospital)

Project summary:  Decisions on the most appropriate treatment for diseases such as colorectal cancer and diverticulitis can be complex. Advanced imaging such as MRI and CT can provide information on the location of the disease compared to other anatomy and also functional information on the disease and surrounding organs. There is also the potential to gain additional information from these images using techniques such as radiomics. At Liverpool hospital, there is a database of previous patient histories, including outcome as well as imaging information which we can use in collaboration with medical specialists. This project will use machine learning and deep learning approaches to determine anatomical and disease boundaries and combine them with clinical and response data to model treatment response and develop treatment decision support tools. The incoming PhD student should ideally have a computer science qualification with research skills and an interest to develop deep learning and decision support techniques in the medical imaging field. Research in this area is subject to ethics approvals and institutional agreements.

For more information contact:  [email protected][email protected]

Supervisor team:  Prof. Arcot Sowmya, Prof. Padmaja Sankaridurg (Brien Holden Vision Institute)

Project summary:  The prevalence of myopia (short-sightedness) is expected to rise dramatically to affect nearly 50 per cent of the global population by the year 2050. With the increase in myopia, the prevalence of high myopia, which is associated with greater risks of permanent ocular complications, is also expected to rise significantly and reach a global prevalence of 9.5 per cent by the year 2050. Due to the increasing burden of the myopia epidemic, there is a need to identify children at risk of myopia and high-myopia so as to intervene early. A large amount of data is being collected for studying the incidence and progression of myopia in children, which has created an opportunity to analyse the multi-modal data using data mining, image analysis and deep learning techniques. The aim of the PhD project is to use multimodal data sources and advanced data mining techniques to predict the onset and progression of myopia. The incoming PhD student should ideally have a computer science qualification with research skills and an interest to apply data mining and deep learning techniques in the ocular field. Research in this area is subject to ethics approvals and institutional agreements.

For more information contact:  [email protected][email protected]i.org

Supervisor team:  Prof. Arcot Sowmya and Dr Simone Reppermund

Depression and self-harm represent substantial public health burdens in the older population. Depression is ranked by the WHO as the single largest contributor to global disability and is a major contributor to suicide. This project will use large linked administrative health datasets to examine health profiles, service use patterns and risk factors for suicide in older people with depression. Given the vast amount of data included in linked datasets, new ways of analysing the data are necessary to capture all relevant data signals. This project will generate a sound epidemiological and service evidence base that informs our understanding of health profiles and service system pathways in older people with depression and risk factors for trajectories into suicide.

For more information contact:  [email protected][email protected]

Data & knowledge research group

Supervisory team:  Xuemin Lin, Wenjie Zhang 

Project summary:  Efficient processing of large scale multi-dimensional graphs.

This project aims to develop novel approaches to process large scale graphs such as social networks, road networks, financial networks, protein interaction networks, etc. The project will focus on the three most representative types of problems against graphs, namely cohesive subgraph computation, frequent subgraph mining and subgraph matching. The applications include anomaly detection, community search, fraud and crime detection.  

For more information contact:  [email protected]  or  [email protected]   

Supervisory team:  Wei Wang, Xin Cao 

Project summary:  The immense popularity of online social networks has resulted in a rich source of data useful for a wide range of applications such as marketing, advertisement, law enforcement, health and national security, to name a few. Ability to effectively and efficiently search required information from huge amounts of social network data is crucial for such applications. However, current search technology suffers from several limitations such as inability to provide geographically relevant results, inadequately handling uncertainty in data and failing to understand the data and queries, resulting in inferior search experience. This project aims to develop a next-generation search system for social network data by addressing all these issues. 

For more information contact:  [email protected]  or  [email protected]   

Supervisory team:  Sri Parameswaran 

Project summary:  Reliability is becoming an essential part in embedded processor design due to the fact that they are used in safety critical applications and they need to deal with sensitive information. The first phase in the design of reliable embedded systems involves the identification of faults that could be manipulated into a reliability problem. A technique that is widely used for this identification process is called fault injection and analysis. The aim of this project is to develop a fault injection and detection engine at the hardware level for an embedded processor. 

For more information contact:  [email protected]

Networked systems and security

Supervisory team:  Sanjay Jha, Salil Kanhere 

Project summary:  This project aims to develop scalable and efficient one-to-many communication, that is, broadcast and multicast, algorithms in the next generation of WMNs that have multi-rate multi-channel nodes. This is a significant leap compared with the current state of the art of routing in WMNs, which is characterised by unicast in a single-rate single-channel environment. 

For more information contact:  [email protected]

Supervisory team:  Mahbub Hanssan 

Project summary:  A major focuses of the Swimnet project will be to look at a QoS framework for multi-radio multi-channel wireless mesh networks. We also plan to develop traffic engineering methodologies for multi-radio multi-channel wireless mesh networks. Guarding against malicious users is of paramount significance in WMN. Some of the major threats include greedy behaviour exploiting the vulnerabilities of the MAC layer, location-based attacks and lack of cooperation between the nodes. The project plans to look at a number of such security concerns and design efficient protection mechanisms (Mesh Security Architecture). 

For more information contact:  [email protected]   

Supervisory team:  Wen Hu  

Project summary:  The mission of the SENSAR (Sensor Applications Research) group is to investigate the systems and networking challenges in realising sensor network applications. Wireless sensor networks are one of the first real-world examples of "pervasive computing", the notion that small, smart and cheap, sensing and computing devices will eventually permeate the environment. Though the technologies still in their early days, the range of potential applications is vast - track bush fires, microclimates and pests in vineyards, monitor the nesting habits of rare sea-birds, and control heating and ventilation systems, let businesses monitor and control their workspaces, etc. 

For more information contact:  [email protected]

Service orientated computing

Supervisory team:  Boualem Benatallah, Lina Yao, Fabio Casati

Project summary:  This project investigates the significant and challenging issues that underpin the effective integration of software-enabled services with cognitive and conversational interfaces. Our work builds upon advances in natural language processing, conversational AI and services composition.

We aim to advance the fundamental understanding of cognitive services engineering by developing new abstractions and techniques. We’re seeking to enable and semi-automate the augmentation of software and human services with crowdsourcing and generative model training methods, latent knowledge and interaction models. These models are essential for the mapping of potentially ambiguous natural language interactions between users and semi-structured artefacts (for example, emails, PDF files), structured information (for example, indexed data sets), apps and APIs.

For more information contact:  [email protected]  or  [email protected]

Supervisory team:  Lina Yao and Defence Science & Technology Group

Project summary:  This research is supported by the Defence Science and Technology Group. It aims to develop intelligent methodologies to capture the environment in sufficient fidelity to evaluate (model and predict) what application/system changes need to occur to fulfil the requirements (goals) of the mission.

For more information contact:  [email protected]

Supervisory team:  Lina Yao, Boualem Benatallah and Quan Z. Sheng

Project summary:  The overall goal of this project is to develop novel machine learning and deep learning techniques that can accurately monitor and analyse human activities. These techniques will monitor and analyse daily living on a real-time basis and provide users with relevant, personalised recommendations, improving their lifestyle through relevant recommendations.

For more information contact:  [email protected]  or  [email protected]

Supervisory team:  Lina Yao

Project summary : This project is supported by Office of Naval Research Global (US Department of Navy). The aim of this project is to develop a software package for resilient context-aware human intent prediction for human-machine cooperation.

Supervisory team:  Lina Yao and Xiwei Xu

Project summary:  The research is supported by our collaborative research project with Data61. The aim is to develop an integrated end-to-end framework for fostering trust in Federated/Distributed AI systems.

For more information contact:  [email protected]  or  [email protected]

Supervisory team:  Helen Paik

Project summary:  Micro-transactions stored in blockchain create transparent and traceable data and events, providing burgeoning industry disruptors an instrument for trust-less collaborations. However, the blockchain data and its’ models are highly diverse. To fully utilise its potential, a new technique to efficiently retrieve and analyse the data at scale is necessary.

This project addresses a significant gap in current research, producing a new data-oriented system architecture and data analytics framework optimised for online/offline data analysis across blockchain and associated systems. The outcome will strongly underpin blockchain data analytics at scale, fostering wider and effective adoption of blockchain applications. A scholarship/stipend may be available.

For more information contact:  [email protected]

Supervisory team:  Fethi Rabhi and Boualem Benatallah

Project summary:  All modern organisations use some form of analytics tools. Configuring, using and maintaining these tools can be very costly for an organisation. Analytics tools require expertise from a range of specialties, including business insight, state-of-the-art modelling approaches and tools such as AI and machine learning as well as efficient data management practices. A knowledge engineering approach can deliver flexible and custom data analytics applications that align with organisational objectives and existing IT infrastructures. This model uses existing resources and knowledge within the organisation. The project uses semantic-web based knowledge modelling techniques to build a comprehensive view related to an organisation’s analytics objectives while leveraging open knowledge and open data to expand its scope and reduce costs.

We aim to help organisations utilise and reuse public and organisational knowledge efficiently when conducting data analytics. Our work also involves the rapid development and deployment of analytics applications that suit emerging analytics needs, plugging new data and software on-demand using new approaches such as APIs and cloud services. The proposed techniques have already been piloted in the areas of house price prediction in collaboration with the NSW Government and portfolio management in collaboration with Ignition Wealth.

For more information contact:  [email protected]  or  [email protected]

Theoretical computer science

Supervisory team:  Ron van der Meyden 

Project summary:  The technology of cryptocurrency and its concepts can be broadly applicable to range of applications including financial services, legal automation, health informatics and international trade. These underlying ideas and the emerging infrastructure for these applications is known as ‘Distributed Ledger Technology’. 

For more information contact:  [email protected]   

Trustworthy systems

Supervisory team:  Gernot Heiser, June Andronick 

Project summary:  seL4, the secure embedded L4 microkernel, is a key element of our research program. We developed seL4 to provide a reliable, secure, fast and verified foundation for building trustworthy systems. seL4 enforces security within componentised system architectures by ensuring isolation between trusted and untrusted system components and by carefully controlling software access to hardware devices in the system. 

For more information contact:  [email protected]  or  [email protected]

Projects with top up scholarship for domestic students


Project description:

Previous studies have shown that cognitive training can effectively improve people's skillsets and emotional capabilities in cognitive deficits. Such training programs are known to enhance the participants' brain health and better prepare them for an independent life. However, the existing conventional technologies for such training are not scalable and lack personalized features to optimize the efficacy. In this project, we will develop a technology platform for automatically acquiring and processing multimodal training data. The project will be conducted in collaboration with Stronger Brains, a not-for-profit organization that provides cognitive training. We aim to develop a fully automated social and cognitive function assessment framework based on multimodal data. Such a framework is essential to establish a  system with less involvement of experts and increase its scalability. The project involves:

The fields of Science, Technology, Engineering and Math, otherwise known as STEM, play a key role in the sustained growth and stability of any economy and are a critical component in shaping the future of our society. This project aims to develop new evidence-based guidelines for designing highly effective teaching simulations for a STEM subject that personalizes training to learner proficiency. In particular, we aim to design a novel AI-powered framework for dynamic adaptive learning in STEM educational technology to improve learning outcomes in an accessible and engaging environment. The potential contributions of the project involve:

Supervisor:  Dr Rahat Masood ( [email protected] )

Supervisory team:  Prof Salil Kanhere (CSE - UNSW), Suranga Seneviratne (USyd), Prof Aruna Seneviratne (EE&T – UNSW)

Children start using the Internet from a very early age for entertainment and educational purposes and continue to do so into their teen years and beyond. In addition to providing the required functionality, the online services also collect information about their users, track them, and provide content that may be inappropriate such as sexually explicit content; content that promotes hate and violence, and other content compromising users’ safety. Another major issue is that there is no established mechanism to detect the age of users on online platforms hence, leading children to sign up for services that are inappropriate for them. Through this research work, we aim to develop an age detection framework that can help detect children’s activities on online platforms using various behavioural biometrics such as swipes, keystrokes, and handwriting. The core of this project revolves around the ground-breaking idea that “User Touch Gestures” contain sufficient information to uniquely identify them, and the “Touch Behaviour” of a child is very different from that of an adult, hence leading to child detection on online platforms. The success of this project will enable online service providers to detect the presence of children on their platforms and offer age-appropriate content accordingly.

Users unintentionally leave digital traces of their personal information, interests and intents while using online services, revealing sensitive information about them to online service providers. Though, some online services offer configurable privacy controls that limit access to user data. However, not all users are aware of these settings and those who know might misconfigure these controls due to the complexity or lack of clear instructions. The lack of privacy awareness combined with privacy breaches on the web leads to distrust among the users in online services. Through this research study, we intend to improve the trust of users on the web and mobile services by designing and developing user-centric privacy-preserving solutions that involve aspects of user privacy settings, user reactions and feedbacks on privacy alerts, user behavioural actions and user psychology. The aforementioned factors will be first used in quantifying privacy risks and later used in designing privacy-preserving solutions. In essence, we aim to improve privacy in mobile and web platforms by investigating various human factors in: i) privacy risk quantification and assessment, and ii) privacy-preserving solutions.

Deep learning techniques have shown great success in many applications, such as computer vision and natural language processing. However, in many cases, purely data-driven approaches would provide suboptimal results, especially when limited data are available for training the models. This dependency on large-scale training data is well understood as the main limitation of deep learning models. One way to mitigate this problem is to incorporate knowledge priors into the model, similarly to how humans reason with data; and there are various types of knowledge priors, such as data-specific relational information, knowledge graphs, logic rules and statistical modelling. In this PhD project, we will investigate novel methods that effectively integrate knowledge priors and commonsense reasoning with deep learning models. Such models can be developed for a wide range of application domains, such as computer vision, social networks, biological discovery and human-robot interaction.

Deep learning models are typically considered a black-box, and the lack of explainability has become a major obstacle to deploy deep learning models to critical applications such as medicine and finance. Explainable AI has thus become an important topic in research and industry, especially in the deep learning era. Various methods for explaining deep learning models have been developed, and we are especially interested in explainability in graph neural networks, which is a new topic that has emerged very recently. Graph neural networks are becoming increasingly popular due to their inherent capability of representing graph structured data, yet their explainability is more challenging to explore with the irregular and dynamic nature of graphs. In this PhD project, we will investigate novel ways of modelling explainability in graph neural networks, and apply this to various applications, such as computer vision, biological studies, recommender systems and social network analysis.

Due to the graph’s strong expressive power, a host of researchers are turning to graph modelling to support real-world data analysis. Given the prevalence of graph structures with temporal information in user activities, temporal and dynamic graph processing is an important and growing field of computer science. Driven by a wide spectrum of applications, such as recommendation and fraud detection in e-commerce, and malicious software detection in cybersecurity, this project aims to develop novel techniques for scalable and efficient temporal graph processing. The specific focus is to tame the challenges brought by the large volume, the high velocity, the complex structure of big temporal and dynamic graphs. The project will lay theoretical foundations and deliver substantial outcomes including computing frameworks, novel indexes and incremental and approximate algorithms to process large-scale graphs.

Supervision team

Most cyber threat intelligence platforms provide scores and metrics that are mainly derived from open-source and external sources. Organisations must then figure out if and how the output is relevant to them.

Research problems

Continuous monitoring and calculation of an organisation’s ‘Threat Risk’ posture score using a range of internal and external intelligence.

A curated cyber and threat newsfeed that is relevant to an organisation. The source of the newsfeed will leverage the internal and external analysis from the first question. The output will include information that helps users understand and digest their organisation’s threat posture in a non-technical manner.

Proposed approaches

We propose to develop dynamic GNN models for discovering dynamic cyber threat intelligence from blended sources. GNN has achieved state-of-the-art performance in many high-impact applications, such as fraud detection, information retrieval, and recommender systems, due to their powerful representation learning capabilities. We propose to develop new GNN models which can take blended intelligence sources into account in the threat intelligence prediction. Moreover, many GNN models are static that deal with fixed structures and parameters. Therefore, we propose to develop dynamic GNN models which can learn the evolution pattern or persistent pattern of dynamic graphs.

Top Computer Science Ph.D. Programs

ComputerScience.org Staff

Contributing Writer

Learn about our editorial process .

Updated September 9, 2022

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Are you ready to discover your college program?

A doctorate in computer science is the highest degree in the field of computer and information technology. Doctoral programs teach students to conduct scientific studies of computation, coding languages, and algorithms -- the step-by-step procedures that make computers perform tasks when converted into a programming language.

Programmers use algorithms as the foundation of familiar software, such as operating systems, internet browsers, and smartphone applications. More specifically, modern-day innovations work by leveraging algorithms to match Uber drivers to passengers, calculate delivery routes for UPS, and detect credit card fraud.    

As the need for tech innovations expands, the demand for employees with advanced knowledge of computer science similarly increases. The Bureau of Labor Statistics (BLS) projects a 15% growth in computer science research jobs from 2019-2029, much faster than the average for all occupations.

This page contains descriptions of some of the top doctoral programs in computer science. It also details information about choosing the right doctoral program in computer science, how to gain admission into a Ph.D. program, and available jobs and salaries for graduates in the field.

Why Get a Doctorate in Computer Science?

Computer science is the scientific study of computational processes, programming languages, and algorithms. Unlike computer engineers, computer scientists do not usually design or build computer hardware, such as computer processors, hard drives, or video cards. Rather, these professionals write code, design algorithms, and study the informational processes and procedures that make computers function.

Employment opportunities vary by degree level. Computer scientists with associate, bachelor's, or master's degrees tend to perform programming-related tasks, such as writing or testing new code for software products.

Graduates with doctoral degrees perform innovative research, such as devising new algorithms and computational theories.

The computer science discipline has yielded groundbreaking innovations, such as the first personal computer, the internet, and the smartphone. Many learners pursue a Ph.D. in computer science because they aspire to discover new technology to revolutionize our daily lives.

Below, we consider some additional reasons for pursuing a doctorate in computer science.

Top Online Programs

Explore programs of your interests with the high-quality standards and flexibility you need to take your career to the next level.

What To Expect From Computer Science Doctoral Programs

To obtain a doctorate in computer science, students need to take around 75 graduate credits, including 20 dissertation credits. Most programs allow enrollees to transfer 30 credits of prior computer science graduate coursework, which may help cut costs and limit time away from the job market.

Degree length varies by program format. A typical Ph.D. in computer science takes around five years to complete. However, learners with a prior master's in the field can finish in 3-4 years. Most reputable universities also offer part-time tracks, which can add a few years to the degree timeline. 

While undergraduates in computer science spend a lot of time writing code, doctoral students typically dive into advanced topics, such as machine learning, artificial intelligence, and computer vision. Postgraduates specializing in systems coding take intensive programming classes and address design challenges, such as building networks, routers, and operating systems.

Doctoral Admission Requirements

Admission into a doctoral program in computer science typically requires a bachelor's or master ' s in computer science , although some programs may accept applicants with associate degrees in computer science and bachelor's degrees in other fields.

A doctoral program candidate must submit an online application package. Typical application materials include a CV, transcripts, letters of recommendation, a statement of purpose, a writing sample or design project, and GRE scores.

Most doctoral programs in computer science do not require a specific GPA or minimum GRE scores, but prospective students should aim for GRE scores in the low 90th percentile or higher and unweighted GPAs of at least 3.0-3.5. Admissions departments may consider applicants with low GPAs if they demonstrate improvement over time.

Computer Science Degree and Specialization Options

Computer science students at the undergraduate and master's levels learn to design algorithms and develop computation theories. Doctoral programs then build on students' previous education, allowing them to dig deep into their specializations within the computer science field.

Computer science doctoral students graduate with a thorough understanding of computer science theory and research, often specific to a narrow area of study.

These learners may specialize in automated algorithmic process management, advanced embedded systems, or any of the three popular concentrations detailed below:

Human-Computer Interaction

Programming Languages

Artificial Intelligence

Popular Doctoral Program Courses

Course availability varies by school. In most Ph.D. programs, each student needs to complete around 50 credits, including qualifying exam credits, before starting their dissertation. A typical curriculum contains mandatory classes, electives, and concentration seminars. The following list provides examples of popular courses in doctoral computer science programs:

The Doctoral Dissertation

A Ph.D. in computer science culminates in a dissertation, a lengthy research project that addresses a theoretical problem in computer science. Some programs allow a student to complete three related research papers instead of a traditional dissertation.

Learners conduct dissertation research in close consultation with their supervisors and dissertation committees. Most computer science programs require students to pass a qualifying exam before beginning the dissertation.

After completing the dissertation, the supervisor organizes an oral defense. Doctoral candidates present their dissertation research, and the dissertation committee and 1-2 external examiners take turns questioning the examinee.

How Much Will a Doctorate in Computer Science Cost?

The cost of a doctorate in computer science depends on factors like state residency, degree format, and available funding.

While most universities charge higher out-of-state tuition than in-state tuition, they often provide online programs at a reduced cost, regardless of state residency. The total cost of tuition for an online doctoral degree in computer science can range from $27,000-$60,000 .

That said, most doctoral programs offer tuition waivers and/or stipends in exchange for part-time work as teaching aids or research assistants. Schools often guarantee such funding to doctoral students for at least a portion of their time studying.

The following links provide additional information on financing options, such as grants, financial aid, and student loans.

Jobs and Salaries for Doctors of Computer Science

While graduates with bachelor's or master's degrees qualify for entry-level jobs in computer science, corporate research positions and university and college professorships normally require each candidate to possess a Ph.D.

BLS data indicates a median salary of $122,840 for computer and information research scientists, along with a projected growth rate of 15% from 2019-2029. A graduate with a Ph.D. in computer science earns a higher salary than those who only have master's or bachelor's degrees. Considering all occupations, the median annual salary for hires with doctoral degrees reaches around 30% higher than the national median for those with bachelor's or master's degrees.

The following section includes information about potential careers for graduates with doctorates in computer science.

University Professor of Computer Science

University professors of computer science at the assistant, associate, or tenured level conduct research in computer science, serve on committees, and teach computer science courses. Other duties include presenting at conferences, publishing work in peer-reviewed journals, and supervising Ph.D. students.

Computer Network Architect

Computer network architects design and build data communication networks, such as intranets, local area networks, wide area networks, and cloud infrastructures. Typical job duties include researching novel networking technologies, creating layouts for data communication networks, and upgrading hardware and software.

Computer and Information Research Scientist

Computer and information research scientists invent and design new approaches to computing and find novel uses for existing technology. Typical responsibilities include inventing new user interfaces; solving complex computational problems for bioscientists, engineers, and geoscientists; and conducting experiments to test software systems.

Software Developer

Software developers design and test systems and applications for computers and handheld devices. Typical job duties include designing new software, testing software performance against specifications, and implementing and updating systems and applications.

How To Find the Right Computer Science Program

Prospective doctoral students in computer science should consider several factors before applying to programs. The most important factor is accreditation. The U.S. Department of Education recognizes six regional accrediting bodies . Regional accreditation pertains to the college or university as a whole. Attending an accredited university guarantees that the school meets rigorous educational standards. 

Programmatic accreditation ensures that specific degrees within schools meet strict standards. Prospective computer science students should select a program that carries programmatic accreditation from ABET .  

Candidates should also determine whether the faculty's research interests align with their own. Ph.D. students eventually need to complete dissertations under the supervision of faculty members, and faculty can only properly supervise doctoral students in their focus areas.

Finally, potential students who plan to complete traditional on-campus degrees should give priority to Ph.D. programs that offer tuition waivers and graduate stipends.

Should You Get Your Ph.D. in Computer Science Online?

Long before COVID-19 drove many colleges and universities to move classes online, distance learning saw a significant rise in popularity . Online learning offers unbridled convenience and flexibility, which may appeal to working professionals and those who cannot commit to several years away from family or friends.

Most reputable online learning programs provide a learning experience that simulates the on-campus college experience. Many online programs provide lectures, labs, and alumni events in real time, enabling learners to participate in discussion and networking opportunities.

The prevalence of discounted online degrees enables online learners to obtain doctoral degrees at a reduced cost. Most programs offer tuition-waivers and stipends to on-campus learners, but these wages may not allow students to live comfortably, depending on school location and family commitments.

Top Computer Science Doctoral Programs

Our list of doctoral programs in computer science was culled from the Integrated Postsecondary Education Data System and links to each school's website for more information. Take a look at these institutions to help make the next move on your educational path. All schools on this list hold regional accreditation from one of the following accrediting bodies:

Wright-Patterson AFB, OH

View Program

Waltham, MA

Providence, RI

Pasadena, CA

Pittsburgh, PA

Cleveland, OH

Potsdam, NY

New York, NY

Hanover, NH

Chicago, IL

Philadelphia, PA

Atlanta, GA

Melbourne, FL

Washington, DC

Cambridge, MA

Bloomington, IN

Indianapolis, IN

Bethlehem, PA

Baton Rouge, LA

Houghton, MI

Bozeman, MT

Monterey, CA

Socorro, NM

Greensboro, NC

Raleigh, NC

Evanston, IL

Fort Lauderdale, FL

Corvallis, OR

University Park, PA

Portland, OR

West Lafayette, IN

Carbondale, IL

Stanford, CA

Hoboken, NJ

College Station, TX

San Marcos, TX

Knoxville, TN

El Paso, TX

San Antonio, TX

Medford, MA

Berkeley, CA

Los Angeles, CA

Riverside, CA

La Jolla, CA

Santa Barbara, CA

Santa Cruz, CA

and Engineering

Boulder, CO

Honolulu, HI

Lafayette, LA

Baltimore, MD

College Park, MD

Amherst, MA

Memphis, TN

Minneapolis, MN

Las Vegas, NV

Chapel Hill, NC

Rochester, NY

Columbia, SC

Salt Lake City, UT

Burlington, VT

Seattle, WA

Laramie, WY

Nashville, TN

Pullman, WA

Saint Louis, MO

Morgantown, WV

Kalamazoo, MI

Worcester, MA

Frequently Asked Questions About Computer Science Ph.D's

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Ph.D. Topics in Computer Science (2023)

PhD Topics in Computer Science

While there are many topics, you should choose the research topic according to your personal interest. However, the topic should also be chosen on market demand. The topic must address the common people’s problems.

In this blog post, we are listing important and popular Ph.D. (Research) topics in Computer Science .

PhD in Computer Science 2023: Admission, Eligibility

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The hottest topics in computer science in 2023


Other good research topics for Ph.D. in computer science


Internet of things

Cloud computing

Machine learning

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Dr. Sunny is an Assistant Professor in higher education. He has completed his Ph.D. He has a depth of knowledge in the research field and in higher education.

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100 Great Computer Science Research Topics Ideas for 2022

Computer science research paper topics

Being a computer student in 2022 is not easy. Besides studying a constantly evolving subject, you have to come up with great computer science research topics at some point in your academic life. If you’re reading this article, you’re among many other students that have also come to this realization.

Interesting Computer Science Topics

Awesome research topics in computer science, hot topics in computer science, topics to publish a journal on computer science.

Fun AP Computer Science Topics

Exciting computer science ph.d. topics, remarkable computer science research topics for undergraduates, incredible final year computer science project topics, advanced computer science topics, unique seminars topics for computer science, exceptional computer science masters thesis topics, outstanding computer science presentation topics.

Main Project Topics for Computer Science

Whether you’re earnestly searching for a topic or stumbled onto this article by accident, there is no doubt that every student needs excellent computer science-related topics for their paper. A good topic will not only give your essay or research a good direction but will also make it easy to come up with supporting points. Your topic should show all your strengths as well.

Fortunately, this article is for every student that finds it hard to generate a suitable computer science topic. The following 100+ topics will help give you some inspiration when creating your topics. Let’s get into it.

One of the best ways of making your research paper interesting is by coming up with relevant topics in computer science . Here are some topics that will make your paper immersive:

Your next research topic in computer science shouldn’t be tough to find once you’ve read this section. If you’re looking for simple final year project topics in computer science, you can find some below.

Whenever a topic is described as “hot,” it means that it is a trendy topic in computer science. If computer science project topics for your final years are what you’re looking for, have a look at some below:

Perhaps you’d like to write a paper that will get published in a journal. If you’re searching for the best project topics for computer science students that will stand out in a journal, check below:

 Controversial Topics in Computer Science

Some of the best computer science final year project topics are those that elicit debates or require you to take a stand. You can find such topics listed below for your inspiration:

Are you a computer science student looking for AP topics? You’re in luck because the following final year project topics for computer science are suitable for you.

When studying to get your doctorate in computer science, you need clear and relevant topics that generate the reader’s interest. Here are some Ph.D. topics in computer science you might consider:

Looking for computer science topics for research is not easy for an undergraduate. Fortunately, these computer science project topics should make your research paper easy:

Your dissertation paper is one of the most crucial papers you’ll ever do in your final year. That’s why selecting the best ethics in computer science topics is a crucial part of your paper. Here are some project topics for the computer science final year.

Your instructor may want you to challenge yourself with an advanced science project. Thus, you may require computer science topics to learn and research. Here are some that may inspire you:

When searching for computer science topics for a seminar, make sure they are based on current research or events. Below are some of the latest research topics in computer science 2020:

Are you looking for computer science thesis topics for your upcoming projects? These topics below are meant to help you write your best paper yet:

A computer science presentation requires a topic relevant to current events. Whether your paper is an assignment or a dissertation, you can find your final year computer science project topics below:

 Key Computer Science Essay Topics

You may be pressed for time and require computer science master thesis topics that are easy. Below are some topics that fit this description:

One major mistake students make when writing their papers is selecting topics unrelated to the study at hand. This, however, will not be an issue if you get topics related to computer science, such as the ones below:

We Can Help You with Computer Science Topics, Essays, Thesis, and Research Papers

We hope that this list of computer science topics helps you out of your sticky situation. We do offer other topics in different subjects. Additionally, we also offer professional writing services tailor-made for you.

We understand what students go through when searching the internet for computer science research paper topics, and we know that many students don’t know how to write a research paper to perfection. However, you shouldn’t have to go through all this when we’re here to help.

Don’t waste any more time; get in touch with us today and get your paper done excellently.

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50+ best computer science topics for you.

April 3, 2020

computer science topics

Computer science is a very vast field, and this makes getting computer science topics quite tricky. Computer scientists study computers and computational systems. Unlike professionals such as electrical and computer engineers, a computer scientist focuses chiefly on software and software systems studying the theory, design, development, and application of these software and software systems.

The significant areas of study in computer science include human-computer interaction, artificial intelligence, security, database systems, and lots more. Here are some topics in computer science from which you can choose!

Computer Science Essay Topics

If you want to write an engaging computer science essay, it is very crucial to choose interesting computer science topics that are fun to write about, and which your readers will find captivating. That said, below is a list of interesting topics in computer science to assist you in writing an excellent essay! If you need qualified writing assistance we advise you to check our essay writers .

Computer Science Research Topics

Research paper topics in computer science are in constant need because computer science research is a continually advancing field of scientific research. With so many novel concepts in computer science, research continues to thrive in many aspects. As you already know, choosing research topics in computer science is not a walk in the park. We have carefully selected some computer science research papers topics and computer science research topics for undergraduates for you to consider for your research.

AP Computer Science Topics

The AP Computer Science A exam aims to test students how much a student knows about Java. It is the equivalent of a first-semester course in computer science, and students who would like to challenge themselves and engage in a technical major in college will find the exams interesting. Here is the general overview of the AP computer science topics you would have to study to pass the exams.

Controversial Topics In Computer Science

Most times, controversies surround newfound applications of specific findings. This theory still holds even in computer science. We bring you five current computer science controversial topics that will guide you in your exploration for more.

Computer Science Project Topics

When project ideas fully develop, they could lead to great discoveries. This is why we have carefully crafted the best project topics for computer science students. Check out the project ideas and work on the one you find most promising!

Computer Science Thesis Topics

Are you a masters student looking for a worthwhile computer science research paper topic? Look no further! Here is a list of research topics in computer science for your thesis.

PhD Topics In Computer Science

For a PhD, you need to streamline your search to address pertinent issues. This is why we have crafted five PhD research topics in computer science for you to consider.

Computer Science Presentation Topics

Without the most engaging of topics, an audience listening to you during a presentation will most likely get distracted or tune off completely. If you want to keep your audience alive, here are some excellent computer science presentation topics you should consider.

Hot Topics In Computer Science

Everyone loves to stay up to date with current trends. This is why we bring you some current topics in computer science that are worth your while!

So here we are! You now have the luxury of 50 computer science topics to choose from, be it to write an essay, conduct research, carry out a project, and so on! Go and be the best that you can be! Also, you can check out our capstone project ideas .

Statistics Project Ideas

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Artificial intelligence research topics for phd manuscripts 2021, introduction.

Imagine a world where knowledge isn’t limited to humans!!! A world in which computers will think and collaborate with humans to create a more exciting universe. Although this future is still a long way off, Artificial Intelligence has made significant progress in recent years. In almost every area of AI, such as quantum computing, healthcare, autonomous vehicles, the internet of things, robotics, and so on, there is a lot of research going on. So much so that the number of annual Published Research Papers on Artificial Intelligence has increased by 90% since 1996.

phd project topics in computer science

Keeping this in mind, there are several sub-topics on which you can concentrate if you want to study and write a thesis on Artificial Intelligence. This article covers a few of these subjects and provides a short overview. Here some of the recent Research Topics ,

Deep Learning

Deep Learning is a type of machine learning that learns by simulating the internal workings of the human brain in order to process data and make decisions.Deep Learning is a form of machine learning that employs artificial neural networks. These neural networks are linked in a web-like structure, similar to the human brain’s networks (basically a condensed version of our brain!).

Artificial neural networks have a web-like structure that allows them to process data in a nonlinear manner, which is a major advantage over conventional algorithms that can only process data in a linear manner. Rank Brain, one of the variables in the Google Search algorithm, is an example of a deep neural network.

Recent research topics

phd project topics in computer science

Reinforcement Learning

Reinforcing Learning is an aspect of Artificial Intelligence in which a computer learns something in the same way as humans do. Assume the computer is a student, for example. Over time, the hypothetical student learns from its errors. As a outcome of trial and error, Reinforcement Machine Learning Algorithms learn optimal behaviour.

This means that the algorithm determines the next way to proceed by learning behaviours based on its current state that will increase the reward in the future. This also works for robots, just as it does for humans!

Google’s AlphaGo Computer Programme , for example, used Reinforcement Learning to defeat the world champion in the game of Go (a human!) in 2017.

Robotics is an area concerned with the creation of humanoid robots that can assist humans and perform several acts. In certain cases, robots can behave like humans, but can they think like humans as well?

Kismet, a social interaction robot developed at M.I.T.’s Artificial Intelligence Lab, is an example of this. It understands human body language as well as our voice and responds to them appropriately. Another example is NASA’s Robonaut, which was designed to assist astronauts in space.

Natural Language Processing

Humans can obviously communicate with each other by speech, but now machines can as well! This is known as Natural Language Processing, and it involves machines analysing and understanding language and expression as it is spoken (which means that if you speak to a computer, it might only respond!).  Speech recognition, natural language production, natural language translation, and other aspects of NLP are all concerned with language. NLP is recently very important in customer service applications, particularly chatbots. These chatbots use machine learning and natural language processing to communicate with users in textual form and respond to their questions. As a result, you get a personal touch in your customer service experiences without actually speaking with a human.

Here are several research papers in the field of Natural Language Processing that have been published. You can look at them to get more ideas for research and thesis topics on this subject.

Computer Vision

The internet is full of images! This is the selfie age, and taking and posting a photo has never been easier. Each day, millions of images are uploaded to the internet and viewed. It’s important for computers to be able to see and understand images in order to make the most of the vast amount of images available online. And, while humans can do this without thinking about it, computers find it more difficult! This is where Computer Vision enters the image.

To extract information from images, Computer Vision utilizes Artificial Intelligence. This knowledge may include object detection in the image, image content recognition to group images together, and so on. Navigation for autonomous vehicles using images of the surroundings is one use of computer vision, such as AutoNav, which was used in the Spirit and Opportunity rovers that landed on Mars.

Recommender Systems

Do you get movie and series recommendations from Netflix based on your previous choices or favourite genres? This is achieved by Recommender Systems, which offer you advice about what to do next from the vast array of options available online. Content-based Recommendation or even Collaborative Filtering may be used in a Recommender System.

The content of all the products is analysed in Content-Based Recommendation. For example, based on Natural Language Processing performed on the books, you might be recommended books that you may enjoy. Collaborative Filtering, on the other hand, analyses your past reading behaviour and then recommends books based on it.

Internet Of Things

Artificial intelligence is concerned with the creation of systems that can learn to perform human-like tasks based on prior experience and without the need for human interaction. The Internet of Things, on the other hand, is a network of different devices linked to the internet and capable of collecting and exchanging data.

All of these IoT devices now generate a large amount of data, which must be collected and mined in order to produce actionable results. Artificial Intelligence enters the picture at this stage. The Internet of Things is used to collect and manage the massive amounts of data that Artificial Intelligence algorithms need.  As a consequence, these algorithms transform the data into useful actionable results that IoT devices can use.

In this blog discussed the recent enhancement for artificial intelligences and their sub field. This will help to the PhD scholar who are interested to research in artificial intelligences domain.

phd project topics in computer science

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phd project topics in computer science

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Phd project topics in computer science 2022.

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Hands-on experience in a research center is a good way to expand intellectual horizons. The research center exposes one to broaden ideas which are likely to equip over the professional life.

At research center, one has an excellent opportunity to rapidly improve the technical skills in a realistic setting and can develop high-risk ideas which are far ahead of the state-of-the-art in industry. The innovative project completed through a research project center will boost the morale of the student.

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Research Project Topics in Computer Science for PhD

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12 Interesting Computer Science Project Ideas & Topics For Beginners

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In this article, you will learn 12 Interesting Computer Science Project Ideas & Topics For Beginners.

Read the full article to know more.

Computer Science Project Ideas

Computer Science skills are a highly sought-after skillset in IT/ITeS and STEM-related job roles. Some of the most coveted Computer Science skills in the modern industry include coding, computation, data processing, network information security, web architecture, algorithm design, storage systems & management, and mobile development. Learning these skills opens up new and exciting employment opportunities in the present and future workforce. So, if you are a computer science beginner, the best thing you can do is work on some real-time computer science project ideas . Relevant projects not only improves your practical knowledge but also improves your resume. To gain more weightage, consider our free courses developed to increase your skills in  a short duration.

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We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting computer science project ideas which beginners can work on to put their Python knowledge to the test. In this article, you will find top computer science project ideas for beginners and mini-project topics for CSE 3rd year to get hands-on experience.

But first, let’s address the more pertinent question that must be lurking in your mind: why build computer science projects?

phd project topics in computer science

When it comes to careers in software development, it is a must for aspiring developers to work on their own projects. Developing real-world projects is the best way to hone your skills and materialize your theoretical knowledge into practical experience. But if you want to step up your game and learn real-life industry projects, assignments and case studies check out our Advanced Certificate Programme in DevOps where you can showcase your expertise and skills to potential employers using an e-portfolio.

You will need to acquaint yourself with new tools and technologies while working on a computer science project. The more you learn about cutting-edge development tools, environments, and libraries, the broader will be your scope for experimentation with your projects. The more you experiment with different computer science project ideas, and mini project topics for cse 3rd year, the more knowledge you gain.

Computer Science study encompasses programming , design, analysis, and theory. Hence, Computer Science project ideas involve designing and developing various application-based software products and solutions. So, if you wish to know about a few exciting Computer Science project ideas, this article is just what you need! But, if you want to accomplish more, and gain superiority, consider pursuing our Advanced Certificate Programme in Cyber Security designed for working professionals and provides 1:1 high-performance coaching.

Traditionally, different specialization fields opted for a theoretical and instructions-oriented approach. However, today, most job roles demand professionals who have hands-on industry experience. Computer Science is one such discipline where academic learning does not suffice – students need to undertake practical training through real-world Computer Science projects and assignments. It aims to impart students with practical knowledge of operating computer systems. 

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So, here are a few computer science projects for beginners can work on:

Top 12 Computer Science Project Ideas

This list of computer science project ideas for students is suited for beginners, and those just starting out with Python or Data Science in general or final year project topics for computer engineering in diploma .  These computer science project ideas will get you going with all the practicalities you need to succeed in your career as a software developer.

Further, if you’re looking for computer science project ideas for final year, this list should get you going. So, without further ado, let’s jump straight into some computer science project ideas that will strengthen your base and allow you to climb up the ladder.

1.  Face detection

phd project topics in computer science

It is of high importance and it serves various purposes in many fields. Most importantly, the technology of face detection has increased the surveillance efforts of the authorities. 

Face detection coupled with the technology of biometrics and security has helped to identify people’s faces which have resulted in various processes such as starting an app, security, or guiding what the next action steps of the application would be.

The technology of the face detection use the facial algorithms to identify the reach of facial print. The technology can adapt and recognize which facial features to detect and which one to ignore.

One of the best ideas to start experimenting you hands-on computer science projects for students is face detection software. This project focuses on building a face detection software using the OpenCV library. The face detection program will be modelled in a way that it can detect faces in live stream videos from webcam or video files stored in a PC’s local storage. The software uses pre-trained XML classifiers to detect faces in real-time and track them. You can also use different classifiers to identify various objects through this detection program.

To run this program, you need to install the OpenCV library on your local machine. Also, it would be best if you created appropriate paths for the XML classifier files before executing the program. 

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2.  Online auction system

phd project topics in computer science

The online auction allows the users to procure the benefits of the auction from any geographical location. The sellers can showcase their products or services to the buyers across the locations. This helps in wider reachability and huge expansion of the business. 

Another useful feature of online auction is instant feedback feature that allows the bidders to track the price increase due to higher bidding. 

The bidders or buyers from across the globe can login at any time of the day to track or bid. This way they do not lose out on the opportunity due to different geographical timelines.

In an online auction, buyers and sellers engage in transactional business, wherein buyers purchase items through price bidding. Here, the bids have a starting price and an ending time. Potential buyers who place the highest bidding price for an item are declared the winners and owners of particular items. 

In this project, you will create a secure online auction system using the fraud detection method with binary classification. If a user wants to buy a product through an online auction, they must provide their identification details like PAN number, email address, license number, etc. The system will then screen the users, authenticate, and authorize them. Only authorized users can bid in the auction. The system will be designed to predict fraudulent users in the early stages, thereby eliminating the risk of online fraud and scams. This beginner-level computer science projects will help build a strong foundation for fundamental programming concepts.

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3.  evaluation of academic performance.

phd project topics in computer science

Evaluation of the academic performance allows the institutions to track the student’s performance. This does not only help in enhancing the student’s performance but also improving the teaching techniques and teacher’s performance.

The teachers could chart out teaching objectives that help them in achieving those objectives. This way, the teachers can adapt the successful pedagogy and ignore those pedagogies that do not add value to the student’s performance.

This project involves the creation of an evaluation system that can analyze the academic performance of students by utilizing the fuzzy logic method. In the fuzzy logic method, you will consider three parameters, namely, attendance, internal marks, and external marks, to evaluate the final academic performance of students of an institution. The fuzzy inference system is much more accurate than conventional techniques.

While developing this Computer Science project, make sure that the student information uploaded is correct (devoid of errors). Faulty data entry may lead to inaccurate results. 

4.  e-Authentication system 

There are various types of authentication system such as OTP, password, biometrics, etc.

The authentication system allows for better user experience without having the need of mutliple setups. It is also useful for tightening the security. The enhances security features pave the way for more number of users adapting the technology.

The e-authentication has seen wider adaptability. It is used to access government services, transactional processes, online platforms, and more. The users can secure their identity with the means of e-authentication system, thus providing scope for higher security.

This project focuses on building an e-Authentication system using a combination of QR code and OTP for enhanced security. The e-Authentication system is designed to avoid hacking of accounts through shoulder surfing and misuse of login credentials. To be able to use the system, a user has to first register in the system by entering the basic registration details (name, address, zip code, etc.).

Once the registration is complete, the user can access the login module to authenticate the account by entering the email id and password combination they used during registration. Then, the user can proceed to the next authentication step using either of the two options – QR (Quick Response) code or OTP (One Time Password). As per the option selected by the user, the system will generate a QR Code or an OTP. While the QR code will be sent to the user’s mail id, the OTP will be sent via SMS to the registered mobile number of the user. 

The system randomly generates the QR Code and OTP at the time of login. It makes the login more secure. However, to use this system, one always needs an active Internet connection.

5.  Cursor movement on object motion

This is a project where you will design a cursor that can move through desktop and perform actions based on hand gestures. The system’s object movement will be based on RGB (red, green, and blue) colour – it can detect RGB colour object that will function as the mouse. It would help if you imported the Java AWT library to coordinate with the cursor. The system setting uses a webcam to track the movement of the red, green, and blue objects and based on the object movement patterns, accordingly trigger an event. 

The cursor movement system will acquire a single frame from the video recorded by the webcam and flip the frame for the user to see. It converts the captured image into a binary image wherein the RGB objects will become white. The system further adds a bounding box around the object that the user can move throughout the display.  

6.  Crime rate prediction

There are various benefits attached to the crime rate prediction, such as taking preventive measures, tracking of the culprits, advanced decision-making process, etc.

The methodology allows the decision makers to predict the crime and perform law- enforcement measures to mitigate the repercussions.

This way, the stakeholders can provide satisfaction, increase lifestyle experience and most importantly identify the negative externalities and take appropriate actions to curb them.

The stakeholders can allocate the budget based on the statistic, this helps in effective resource allocation. The concerned agencies can utilize their resources to better use. The crime prediction system helps in faster justice delivery and reduce crime rates. 

This is one of the interesting computer science project ideas to create. As the name suggests, this Computer Science project involves building a prediction system that can analyze and predict the crime rate of a particular location. Naturally, the system needs to be fed with relevant data. It uses the K-means data mining algorithm to predict the crime rate. The K-means algorithm can cluster co-offenders and organized crime groups by detecting relevant crime patterns via hidden links, link prediction, and statistical analysis of crime data. 

It functions somewhat like this – the admin will feed the crime data into the system. The algorithm will analyze crime data stored in a database and extract information and patterns from it. It will then collate the crime groups based on the patterns found in the dataset. The clusters will be made based on factors like where the crime took place, which people were involved in the crime, and when the crime occurred. 

7.  Android battery saver system

phd project topics in computer science

The battery saver project is useful for the users to track the usage of the application. The users can track which of the applications are consuming the maximum energy. 

This way the users can optimize their application management. The optimization of the application can limit the application usage, this end up limiting the battery. 

The battery saver in the mobile phone would also allow the users to procure the list of the applications in one place, the consumption rate is also accurate. 

This is one of the simple computer science projects yet an exciting one. The Android battery saver is designed to analyze the battery usage data from built-in classes and create a consolidated list of apps that drain the power of the Android phone. The system can also determine the battery level of the phone. In situations where the battery level is low, and numerous apps are consuming too much power, this system will trigger an alarm telling the user to force stop or close the apps that are drawing power.

While the battery saver system has no backend, it uses Android Studio as the frontend. Since the system feeds on data from the Android phone, it does not need a backend framework. The primary aim of this battery saver system is to notify users of the apps that are high on power consumption, thereby allowing them to take specific actions to stop battery drainage. 

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8.  symbol recognition .

This is one of the excellent computer science project ideas for beginners. The proposed project seeks to build a system that can recognize symbols inserted by the user. This symbol recognition system leverages an image recognition algorithm to process images and to identify symbols. First, the system converts RGB objects into grayscale images which is then further converted into black and white images. During the process, image processing is applied to remove unwanted objects and environmental interference. The system further uses optical character recognition for recognizing the images with 60-80% accuracy. This is one of the interesting computer science projects. 

In the system, all symbol templates will be stored in a specific directory. The size of each image is fixed to allow the easy recognition of the symbols with accuracy. The templates will remain in black and white form, and the system will create a dataset of these templates. When a user inputs a query image into the system, it will resize the query image, compare the resized image values against the template image values in the dataset, and finally display the result in text format. So, while the system takes inputs as images, it delivers output in a textual form.

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9.  Public news droid

There are various benefits to the public news droid, such as-

This is one of the excellent computer science projects for beginners. The public news droid is an informative software application that informs users about the trending news, occurrences, and interesting events happening in and around their locality. Thus, the idea behind creating this information system is to keep the users informed about the happenings in their vicinity. The system uses Android Studio as the frontend and SQL Server as the backend. 

The system involves two modules, one for the admin and one for the user. The admin monitors the accuracy and relevancy of news and information. For instance, if the admin encounters fake news or app misuse, they can take necessary action to stop the spread of such irrelevant information. On the contrary, users can view news and informative articles only of their respective localities/towns/cities, and they can add news related to any other city. Mentioning  computer science projects can help your resume look much more interesting than others.

To use the app, users need to register into the system to use this app and add all the necessary details. Once the registration process is successful, the user can see the latest news, refresh the app, browse for more information, add new information and upload it (within 450 words), and so on. Users can also add images and title for the news they add. 

10.  Search engine 

phd project topics in computer science

The search engine is highly useful, it helps in bringing the visibility of the brand, target based ad, brand awareness, performance management, increasing of the website traffic and more. 

The brands can make their visibility grow by using proper keywords and using various other methodologies. Moreover, the brands can utilise the search engine to overcome the competition and to grow their business. 

More people are able to see the brands, better its authenticity would be. It will eventually result in the revenue growth of the brand. 

This search engine is developed using web annotation. It is one of the trending computer science projects where when users enter specific words or phrases in a search engine, it automatically fetches the most relevant pages that contain those keywords. Web annotation makes it possible. Web annotation helps to make an application user-friendly. Thanks to web annotation, users can add, modify, and remove information from Web resources without altering the resource itself. 

This project uses web annotation on pages and images. When the user enters words, names, or phrases in the system, it will fetch the information and pictures having the same annotation. Then the system displays a list of results that contain the image or content matching to the user input. For this search engine, you need to use an effective algorithm to generate a query result page/search result records based on users’ queries.

11.  Online eBook maker

One of the best ideas to start experimenting you hands-on computer science projects for students is working on online eBook maker. This online eBook maker will allow users to design and create eBooks free of cost. The system has two modules – admin login and author login. The admin can accept requests from users (authors), check and validate their details, evaluate completed eBooks, and process the request by mailing eBooks to the authors. Users can register in the system using the author login.

After filling in the necessary details, users can create new books, specify the context of books, add the title, number of pages, add a book cover, etc. Existing users can simply log in using their ID and password, and they can either create new books or resume editing the existing (unfinished) eBooks. Authors can keep only three incomplete eBooks at a time, of which they must complete at least one book before starting a new book. 

12.  Mobile wallet with merchant payment

phd project topics in computer science

There are various benefits attached to the mobile wallet, such as-

This can be an interesting and useful computer science project ideas. As you can guess by the name, this is a QR code scanning application designed for handling and facilitating liquid cash transactions between sellers (merchants) and consumers. The aim of building this app is to provide a secure, reliable, and efficient platform for monetary transactions on both ends. Each time, the system generates a unique QR code ID, and all passwords are encrypted using AES Encryption Algorithm. 

There are two parts of this application – an Android application for merchants that can scan the QR code and the other part for the consumer for generating the QR Code. The frontend uses Android Studio, and the backend uses SQL Server. This system functions something like this – when merchants scan the QR code generated by the app, the desired amount is transferred into their wallet that is easily transferable into their bank accounts. As for the consumers, they need to add money to their wallet via their credit/debit cards linked to their bank accounts. They can save the card details for future use. Merchants can also change their personal and bank details. And this is the perfect idea for your next computer science project!

Check out: Java Project Ideas & Topics

Some Bonus A Level Computer Science Project Ideas

Basic Hospital Management System

The hospital management system is useful for managing the resources and operating the hospital effectively. The hospital management infrastructure is useful for managing the patient details, infrastructure management, drugs management, dispensary, etc.

The staff trust the hospital managemet application to run the day-to-day functions. Thus, the technology becomes of high importance.

The health management system facilities in better decision- making and revenue management. Apart from serving the patients, the hospitals have to take care of the revenue for acquiring talented doctors and provide decent health facility. 

This is a programming and database management app designed as a centralized system for hospitals to digitize and handle huge data ( like patient details, appointments made, results of lab tests, patient diagnosis information, etc.). This is one of the best computer science project ideas that can add value to your resume.  

Developing a hospital management system is easy for beginners. A functional and effective hospital management system can be created with the basic knowledge of HTML and CSS. 

The system should be able to receive new entries, store them safely and enable hospital staff and system administrators to access, and use the data. 

You should develop the hospital management system in a way that it should assign a unique ID to each patient registered at the hospital. The system must include all necessary details of hospital staff besides patients in a local database.  

When the data increases, it becomes challenging for the staff and hospital administrator to find the required data of a particular patient or staff. So, it is important to have search functionality to make the search process across thousands of data much easier.  

While it is enough to use the local storage to run the hospital management, you can also use a cloud database. Both of them have their pros and cons. You must leverage the advantages and disadvantages to make the computer science topics more challenging and interesting. Check out this Github project for reference.

Real-time Weather Forecasting app

This is a beginner-level web development & programming app that will serve best as a mini project topic for CSE third-year students or a final-year project for those pursuing diplomas in Computer science. The main objective of the app is to create a web-based weather application that can provide real-time weather details (like current temperature and chances of rain) of a particular location. The app can also predict if the day will be rainy, cloudy, or sunny.  

Developing a weather forecasting app is the best way to put your coding skills to the test. To create a weather forecasting app, you will need a stronghold on the basics of web development, HTML, CSS, and JavaScript. For providing the best backend performance, good knowledge of Node.js and express technologies is a must. 

It is important to know how to use API calls to scoop out weather information from other websites and display relevant information in your app.  

For the app’s best User Interface, you have to place an input text box in which the users can enter the location for which weather information is needed. As soon as the search button is hit, the weather forecast for the input location should pop out. Check out this Github project for reference.

It is an interesting app that involves application designing & development, multi-thread processing, socket-programming, and networking.  

Such computer science topics aim at developing a chat application to facilitate instant messaging. Users can create personal accounts in the chat app from where messages can be sent to other chat app users. Check out this Github project for reference.

Wrapping up

These are some cool Computer Science project ideas that you can toy with! Once you finish with these simple computer science projects, and final year project topics for computer engineering in diploma , I suggest you go back, learn a few more concepts and then try the intermediate projects.

When you feel confident, you can then tackle the advanced projects. If you wish to improve your python skills, you need to get your hands on these computer science project ideas . Working on real-world projects allows you to apply your knowledge and skills into practice. Also, if you can create a few of these Computer Science projects, you can add them to your resume – it will definitely help you to stand out among the crowd. I hope you will learn a lot while working on these computer science projects.

If you’re interested to learn more about Java, full-stack software development, check out upGrad & IIIT-B’s  Executive PG Programme in Software Development – Specialisation in Full Stack Development  which is designed for working professionals and offers 500+ hours of rigorous training, 9+ projects, and assignments, IIIT-B Alumni status, practical hands-on capstone projects & job assistance with top firms.

Read our Popular Articles related to Software Development

What is web architecture.

A web architecture is the structure of a website, including its underlying servers, databases, networks, routers, and protocols. It is the design of the system that makes up the World Wide Web. It is also the management of the software and servers used to run websites. Web architecture is an important part of any web presence. It dictates how a user navigates from one website to another, and influences the overall experience. It should focus on providing a positive online experience, and should always be used to enhance the overall user experience, but it should not be confused with the design of the website itself.

How do data mining algorithms work?

Data mining algorithms are a set of software tools and algorithms used to extract information from large amounts of data. They are used to determine which data points are most relevant in a given dataset and are used in a variety-generation algorithm, which is used to generate new lines of data. Data mining algorithms are the steps used to find patterns and trends in large data sets. They are important tools helping organizations make more informed decisions and better serve their customers. Data mining algorithms are used in a wide range of applications, including business intelligence, marketing, and fraud detection. They are also used to understand the behavior of large sets of data, to identify relationships and patterns, and to make predictions.

Why is E-authentication required?

The need for effective e-authentication is due to the fact that users are increasingly using profile verification and sometimes password reset options to protect their accounts on online services, such as social networking sites, and to improve their online security more generally. The use of e-authentication is becoming a common way to prove identity when buying products or services. The process allows users to prove their identity using digital methods instead of traditional documents like ID cards. E-authentication is becoming more and more common, and there are a number of ways it is shaping our digital world.

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phd project topics in computer science

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Phd Topics In Computer Science is a study of transfer of information. PHD scholars of computer science need to base their research topics on their objective area. A certain domain can be selected by them with guidance from their guide or based on their own interest whichever project done by them on PG final year can be more elaborately done in PHD thesis. Most chosen topics for computer science PHD research are grid computing, data mining, remote sensing, mobile computing, wireless communication, image processing, and medical imaging and sensor networks. In order to complete a research work development tools and languages are needed.

Phd Topics In Computer Science areas:

Some of the prominent domains of computer science are as follows:

By choosing these topic researchers can complete their thesis in an effective manner. Many programming languages are involved to create codes and obtain pin point results. Operating system is needed to be selected differently for different areas. Computer programs should process both storage and retrieval. Every information is in data base and obtained in the time of need. Robotic concepts can be obtained by automata theory. Learning and testing can be done by software engineering. Errors in numeric analysis are only solved by computational science.

Hadoop and big data are latest trends in computer science which is preferred by some scholars for their research. It is used to process quite large applications and it minimizes the storage capacity.

Cloud computing:

Java creates and develops cloud computing concepts and it also uses Cloudsim. Cloud computing also performs resource allocation, load balancing, secret key generation and scheduling. Activities of cloud computing applications are energy utilization measurement, secure sharing of patient health records, online banking, and secure file transformation.

Data mining:

It is otherwise known as data warehouse. It helps storing large information which can be obtained anytime and anywhere. Word net tool should be installed for research in order to get English meaning from lexical database. Weka tools is also required to support machine learning process while choosing their projects scholars should also choose objectives such as recommendation, classification and mining process. Both java and dot net is requires to write program languages.

Grid computing:

Gridsim tools build grid computing. It assumes the resources level of a system which becomes the input for processing schedule algorithms FCF8, min-max; genetic algorithm, weighted round robin, max-min and round robin are the needed scheduling algorithms.

Image processing:

Medical imaging and remote sensing are the sub domains of image processing. For medical imaging projects the researcher need to choose a specific human organ to base the project on. To make it as an innovative research algorithm should be upgraded. Remote sensed images of geospace and satellite images are taken as input. MATLAB simulation tool helps in implementation of codes.


Usually PHD scholars choose their research topic based on network. It is an enormous field which covers wireless sensor network, mobile computing and wireless communication. Networking errors are usually solved by many simulation tools, which lead in the creation of new concept. NS2, NS3, OMNET++, QualNet, Opnet and Peer-sim are the needed simulation tools of networking. The results are produced in a graph manner. This graph display parameters of throughput, delay, bandwidth and transmission.Phd Topics In Computer Science

Future enhancement:

Computer vision applications and template matching are the growing domains of computer science. We offer thesis which are more up to date of pattern recognition algorithms.Phd Topics In Computer Science

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Computer Science PhD Projects, Programmes & Scholarships

We have 1,277 computer science phd projects, programmes & scholarships.

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Diversity PhD Scholarships: Fully funded scholarships for UK, black-heritage students

Funded phd programme (uk students only).

Some or all of the PhD opportunities in this programme have funding attached. It is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

PhD Opportunities

PhD Opportunities highlight some of the specific PhD projects, programmes or other information currently available from a university.

Are metal concentrations in the blood potential indicators for multimorbidity?

Phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Funded PhD Project (Students Worldwide)

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Weak and Strong Saturation in Graphs

Calculating neonatal biochemical age at the bedside to transform paediatric nutrition, funded phd project (uk students only).

This research project has funding attached. It is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

Infrared (IR) spectroscopic imaging and digital pathology for development of algorithms for early detection of test-article related lung findings in the Han Wistar rats.

Neural circuits of cooperation and competition, multiscale modelling of individual to collective cell behaviour during normal and pathological angiogenesis, how the cancer ecosystem evolves: from the initiating event to end of life, engineering proteins and organisms using robotics-accelerated evolution, ancient genomics and proteomics, big data and artificial intelligence in inflammatory bowel disease; personalising care through genomics, prediction, and clinical data integration, discovering new medicines with deep learning, competition funded phd project (students worldwide).

This project is in competition for funding with other projects. Usually the project which receives the best applicant will be successful. Unsuccessful projects may still go ahead as self-funded opportunities. Applications for the project are welcome from all suitably qualified candidates, but potential funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Mathematical models of RNA and protein dynamics and their integration with gene expression data

Immersive cyber incident response, young people, urban green and blue space use, and barriers..

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PHD PROJECTS IN COMPUTER SCIENCE , is a broad area as it contains many practical and theoretical concepts. Computer science deals with all concepts that are used in many recent technologies. Research scope in computer science is enormous as its fundamental domains like networking, cloud computing, image processing and data mining is ruling the world. Networking was once used for the purpose of data sharing; today we have reached the peak in networking using technologies like SDN, device to device control, LTE, 5G networks etc.

Computer Science Projects

Data mining concepts also used in search engine to mine data; today we using text mining, image mining, content mining etc. Data warehousing , big data etc are also based on data mining domain. Cloud computing is just start as mode to share information in a broad range, today cloud computing has its latest adaptation like icloud, hybrid cloud etc. If we also take image processing, we cannot say that its useful only for image enhancement. It is also now ruling the world of graphics, multimedia, image security, medical imaging etc. Even in mobile phones, display has also create using image processing algorithms and concepts. In short, we can say also that computer science with its wide domain has wonderful scope for research.

The current phd projects in computer science includes Towards a verified compiler prototype for the synchronous language signal, AAU-star and also AAU Honey jar, advance biometrics, DART, automatic emotion detection, anopheles mosquito comparative genomics , also Distributed computing applications, CGAT: a model immersive personalize training in computational genomics etc.

We can also enumerate thousands of phd in computer-science due to its widespread usage. It is compose uncountable domains and tools. Each tool and also domains have thousands of research area to work. Refer all domains under computer science and choose any topic. We have also provide few tools and its explanation. Researchers can also get a clear cut idea to work in this domain by referring all. If you also have further doubts, contact us anytime as we work 24/7.

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phd project topics in computer science

PhD Topics in Computer Science

Computer Science Topics

The list of PhD topics in Computer Science is unbounded since the discipline is too broad offering multidisciplinary research as it spans from applied areas to theoretical ones. For a successful PhD research, there is a need to identify the more specialized interest to pursue and a refined topic for writing a thesis. The search for a research and thesis topic should be in a right direction because the doctorate is all about doing original research on a reliable thesis topic. Therefore, without thorough information about your interest and the topics, it is almost impossible to do extensive research.

The research topic selection is academically challenging for the student who is not an expert in the concerned discipline and recently embarked on the PhD journey. The dilemma students most often face is to understand how to make a topic reasonable: whether it should touch upon multiple research areas or should only be focusing on simple ideas and theories. We help hundreds of such students through our topic selection services under which our experts collaborate with them to synthesize and refine the information into a concise and unique research topic. Below are provided with the PhD topics in Computer Science formulated and suggested by our experts to demonstrate the quality of our work we deliver to our clients:

For getting such a comprehensive PhD topic for your research, share your requirement and details with our experts by writing us to at [email protected] . We promise to deliver a list of well-researched, unique and customised topics from which you can choose any one which suits your research potential best.


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Find a postgraduate research project in your area of interest by exploring the research projects that we offer in the Department of Computer Science.

We have a broad range of research projects for which we are seeking doctoral students. Browse the list of projects on this page or follow the links below to find information on doctoral training opportunities, or applying for a postgraduate research programme.

Alternatively, if you would like to propose your own project then please include a research project proposal and the name of a possible supervisor with your application.

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201 Computer Science Topics (Updated for 2022)

computer science topics

If you are reading this, you are surely in need of some excellent computer science topics. The good news is that you have arrived at the right place. We have 201 brand new computer science topics that should work great in 2022. The even better news is that each and every one of these research topics in computer science is 100% free to use. You can reword our topics or use them as they are; it’s up to you.

Wondering why you need computer science research paper topics? The truth is that by using the best research paper topics in computer science, you get the chance to win some bonus points from your professor.

After all, who wouldn’t want to read papers on interesting topics in computer science or on some awesome computer science ethics topics? Your professor is bored of reading essays on the same old topics over and over again. Bring something fresh to the table and you’ll immediately stand out from your classmates. If the scope of your work allows, you can also check our technology topics . Without further ado, here is our list of research topics in computer science.

Best Computer Science Research Topics

Writing a research paper can be tough if you don’t pick the right topic. Here are some of the best computer science research topics you can find in 2022:

Easy Topics in Computer Science

If you don’t want to spend too much time working on your paper, we suggest you pick one of our easy topics in computer science:

Computer Security Topics

There are literally thousands of topics to discuss when it comes to computer security. We managed to narrow down the list of computer security topics to only the most interesting of them:

Hot Topics in Computer Science

Are you looking for the newest and most interesting topics? If you are, you should check out our hot topics in computer science:

Computer Science Research Topics for Undergraduates

Undergrads should avoid wasting time searching for topics and simply pick one of these free computer science research topics for undergraduates:

AP Computer Science Topics

Mastering the AP Computer Science A programming class is not easy. Fortunately, we have some AP computer science topics that can help you write a great paper in no time:

Computer Ethics Topics

Yes, there is such a thing as computer ethics. In fact, the subject is pretty vast, so you have plenty of computer ethics topics to choose from:

Computer Science and Robotics Topics

Computer science can be tightly linked to advances in robotics, so why don’t you write about one of our computer science and robotics topics:

Best Project Topics for Computer Science Student

If you are looking for the best project topics for computer science student, you are in luck. We have exactly what you need:

Controversial Topics in Computer Science

There are many controversial topics in computer science, but we managed to pick the best ones. Use any of them for free:

Evolution of Computers Topics

If you are interested in writing about how things evolved since the first computers appeared on the market, we have some interesting evolution of computers topics for you:

Computer Architecture Research Topics

Interested in discussing the functionality, organization and implementation of computer systems? You need our computer architecture research topics:

Computer Science Thesis Topics

If you need to write a thesis in computer science, our writers have some excellent computer science thesis topics for you. Choose one:

Internet of Things Ideas

You’ve probably heard about the IoT, but didn’t really bother to investigate. Check out these Internet of Things ideas and impress your professor:

Quantum Computing Ideas

Truth be told, quantum computing is one of the hottest ideas and works great for 2022. Pick one of our quantum computing ideas for free:

Computer Science Project Topics

So, you are interested in starting a computer science project. Pick one of these computer science project topics for free right now:

Computer Engineering Research Topics

Researching good computer engineering topics can take hours. Why waste your time when we have some computer engineering research topics right here:

Interesting Computer Science Topics

Want to make sure your professor notices your paper? No problem! Simply pick one of these interesting computer science topics:

Computer Networks Topics

Writing about networks and networking never gets old. We have some highly interesting computer networks topics just for you:

Current Topics in Computer Science

You are probably interested in writing about the newest and hottest topics, so here are some current topics in computer science:

Cool Computer Security Research Topics

Do you want to impress your professor and secure a top grade? Pick one of our cool computer security research topics:

Computer Science Presentation Topics

Our team of ENL writers managed to put together an excellent list of computer science presentation topics for you:

PhD Research Topics in Computer Science

Are you looking to start on your PhD but don’t know which topic to choose? We have some ideas of PhD research topics in computer science you might like:

Computer Forensics Research Paper Topics

Ever wonder how law enforcement manages to catch cyber criminals? We have some of the best computer forensics research paper topics right here:

Artificial Intelligence Topics

AI is what everyone’s talking about right now, so it’s the perfect topic for 2022. Fortunately, we have some very nice artificial intelligence topics:

Interesting Cyber Security Ideas

Are you interested in cyber security? It’s an awesome field, we have to admit. Pick one of these interesting cyber security ideas and start writing:

Trends in Computer Science Topics

If you like to analyze trends, computer science is one of the best subjects to try your hand on. Take a look at our trends in computer science topics:

Need More Computer Topics?

Didn’t find the computer topics you were looking for? No problem! In addition to our computer architecture topics, computer science controversial topics and PhD research topics in computer science, we can help students with many others. Professionals providing computer science homework help can quickly put together a list of unique computer related topics for you. All you have to do is ask.

If you need more computer science topics for research or if you just need some simple computer science essay topics, don’t hesitate to contact us. We can send you a list of original computer research topics in no time. Each one of our topics can win you a top grade.

So, what are you waiting for? Get your list of computer science research papers topics right now. Get in touch with us!

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PhD in Computer Science

The PhD in Computer Science is a small and selective program at Pace University that aims to cultivate advanced computing research scholars and professionals who will excel in both industry and academia. By enrolling in this program, you will be on your way to joining a select group at the very nexus of technological thought and application.

Learn more about the PhD in Computer Science .

Forms and Research Areas

General forms.

Dissertation Proposal of Defense Forms

Final Dissertation Defense Forms

All completed forms submitted to the program coordinator.

Research Areas

The Seidenberg School’s PhD in Computer Science covers a wealth of research areas. We pride ourselves on engaging with every opportunity the computer science field presents. Check out some of our specialties below for examples of just some of the topics we cover at Seidenberg. If you have a particular field of study you are interested in that is not listed below, just get in touch with us and we can discuss opportunities and prospects.

Some of the research areas you can explore at Seidenberg include:

Algorithms And Distributed Computing

Algorithms research in Distributed Computing contributes to a myriad of applications, such as Cloud Computing, Grid Computing, Distributed Databases, Cellular Networks, Wireless Networks, Wearable Monitoring Systems, and many others. Being traditionally a topic of theoretical interest, with the advent of new technologies and the accumulation of massive volumes of data to analyze, theoretical and experimental research on efficient algorithms has become of paramount importance. Accordingly, many forefront technology companies base 80-90% of their software-developer hiring processes on foundational algorithms questions. The Seidenberg faculty has internationally recognized strength in algorithms research for Ad-hoc Wireless Networks embedded in IoT Systems, Mobile Networks, Sensor Networks, Crowd Computing, Cloud Computing, and other related areas. Collaborations on these topics include prestigious research institutions world-wide.

Machine Learning In Medical Image Analysis

Machine learning in medical imaging is a potentially disruptive technology. Deep learning, especially convolutional neural networks (CNN), have been successfully applied in many aspects of medical image analysis, including disease severity classification, region of interest detection, segmentation, registration, disease progression prediction, and other tasks. The Seidenberg School maintains a research track on applying cutting-edge machine learning methods to assist medical image analysis and clinical data fusion. The purpose is to develop computer-aided and decision-supporting systems for medical research and applications.

Pattern recognition, artificial intelligence, data mining, intelligent agents, computer vision, and data mining are topics that are all incorporated into the field of robotics. The Seidenberg School has a robust robotics program that combines these topics in a meaningful program which provides students with a solid foundation in the robotics sphere and allows for specialization into deeper research areas.


The Seidenberg School has an excellent track record when it comes to cybersecurity research. We lead the nation in web security, developing secure web applications, and research into cloud security and trust. Since 2004, Seidenberg has been designated a Center of Academic Excellence in Information Assurance Education three times by the National Security Agency and the Department of Homeland Security and is now a Center of Academic Excellence in Cyber Defense Education. We also secured more than $2,000,000 in federal and private funding for cybersecurity research during the past few years.

Pattern Recognition And Machine Learning

Just as humans take actions based on their sensory input, pattern recognition and machine learning systems operate on raw data and take actions based on the categories of the patterns. These systems can be developed from labeled training data (supervised learning) or from unlabeled training data (unsupervised learning). Pattern recognition and machine learning technology is used in diverse application areas such as optical character recognition, speech recognition, and biometrics. The Seidenberg faculty has recognized strengths in many areas of pattern recognition and machine learning, particularly handwriting recognition and pen computing, speech and medical applications, and applications that combine human and machine capabilities.

A popular application of pattern recognition and machine learning in recent years has been in the area of biometrics. Biometrics is the science and technology of measuring and statistically analyzing human physiological and behavioral characteristics. The physiological characteristics include face recognition, DNA, fingerprint, and iris recognition, while the behavioral characteristics include typing dynamics, gait, and voice. The Seidenberg faculty has nationally recognized strength in biometrics, particularly behavioral biometrics dealing with humans interacting with computers and smartphones.

Big Data Analytics

The term “Big Data” is used for data so large and complex that it becomes difficult to process using traditional structured data processing technology. Big data analytics is the science that enables organizations to analyze a mixture of structured, semi-structured, and unstructured data in search of valuable information and insights. The data come from many areas, including meteorology, genomics, environmental research, and the internet. This science uses many machine learning algorithms and the challenges include data capture, search, storage, analysis, and visualization.

Business Process Modeling

Business Process Modeling is the emerging technology for automating the execution and integration of business processes. The BPMN-based business process modeling enables precise modeling and optimization of business processes, and BPEL-based automatic business execution enables effective computing service and business integration and effective auditing. Seidenberg was among the first in the nation to introduce BPM into curricula and research.

Educational Approaches Using Emerging Computing Technologies

The traditional classroom setting doesn’t suit everyone, which is why many teachers and students are choosing to use the web to teach, study, and learn. Pace University offers online bachelor's degrees through NACTEL and Pace Online, and many classes at the Seidenberg School and Pace University as a whole are available to students online.

The Seidenberg School’s research into new educational approaches include innovative spiral education models, portable Seidenberg labs based on cloud computing and computing virtualization with which students can work in personal enterprise IT environment anytime anywhere, and creating new semantic tools for personalized cyber-learning.

PhD Projects in Computer Science Engineering

PhD Projects in Computer Science Engineering is a wide pool of several curious domains. In truth, it aids the scholars who are avid to pursue their research in the  modern-techs . As well as, it grabs the regard for ‘data processing, management, integration, and retrieval.

As a matter of fact,  everything around us is in “computerized format.”  At the same time, we are at the urge to develop  “new software and solutions.”  Thus, it helps you to solve the  existing real-world problems.

Pulsating Fields  – IoT, cloud computing, artificial intelligence, big data, and more

Now, let’s have a glance over some prime concepts for  PhD Projects in Computer Science Engineering  as follows,

Large Scale Data Processing

Computing Proficiency in Cloud

AI and Machine Learning

We have listed 15+ research topics for PhD Projects in computer science engineering . In fact, our experts are keen to reach the best research works in CSE over “18+” years. With this in mind, they continuously contribute their whole life to the research. On the whole, we assure you that we will be “more liable” to give you  inch by inch  assistance.

In any case, if you are in need of our help, then you can easily approach us at your comfy time. We are ready to  lend our hand to guide you in your desired phase .

You will reach great heights in your research when you work with us!!

An efficient mechanism for Datamining Algorithm on Knowledge Dependence scheme

The innovation of Integration of Vehicle Count Data, Bluetooth, and Trasport Model Results via Datamining Techniques

An inventive process of IoT theme for smart datamining-based on environment to unify distributed learning management systems

On the use of Datamining Prediction of Employee Attrition system

A new mechanism for Unveil Black Box for Performance Efficiency of OEE for Semiconductor Wafer Fabrication system

Using Data Mining function of an effectual Survey based on Weather Forecasting scheme

The new mechanism for Adaptive system of Mining Frequent Itemsets Based on Apriori And FP Growth Algorithm

An innovative function of Physical-Data Combined Power Grid Dynamic Frequency Prediction Methodology Based on Adaptive Neuro-Fuzzy Inference System

Effectual function of Analysis and Design for Memristor Crossbar Based on Neuromorphic Intrusion Detection Hardware system

On the use of algorithm C4.5 for used by Classification of posts Twitter traffic jam the city of Jakarta

An innovative process of Artificial Intelligence Assisted for Early recognition System with Machine Learning to support Students Success scheme

A new process of Short-term local weather forecast via dense weather station by deep neural network system

An efficient mechanism for Review of Data Mining Methods based on Bioinformatics system

The novel mechanism for Traffic Hotspots Visualization and Warning System

An inventive process of  knowledge management system intended for analysis of organisational log files

A fresh function of HVAC system used by communication platform

An effectual mechanism for Automation of a paper-based on waste tracking system

A new source of Cybersecurity for healthcare IoT-based systems based Regulation and case-oriented assessment scheme

An innovative function of Identifying Core Concepts for Cybersecurity Results of Two Delphi Methods

A new-fangled mechanism for Parry and RIPOSTE by Honing Cybersecurity Skills with Challenge-Based Workouts

MILESTONE 1: Research Proposal

Finalize journal (indexing).

Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.

Research Subject Selection

As a doctoral student, subject selection is a big problem. Phdservices.org has the team of world class experts who experience in assisting all subjects. When you decide to work in networking, we assign our experts in your specific area for assistance.

Research Topic Selection

We helping you with right and perfect topic selection, which sound interesting to the other fellows of your committee. For e.g. if your interest in networking, the research topic is VANET / MANET / any other

Literature Survey Writing

To ensure the novelty of research, we find research gaps in 50+ latest benchmark papers (IEEE, Springer, Elsevier, MDPI, Hindawi, etc.)

Case Study Writing

After literature survey, we get the main issue/problem that your research topic will aim to resolve and elegant writing support to identify relevance of the issue.

Problem Statement

Based on the research gaps finding and importance of your research, we conclude the appropriate and specific problem statement.

Writing Research Proposal

Writing a good research proposal has need of lot of time. We only span a few to cover all major aspects (reference papers collection, deficiency finding, drawing system architecture, highlights novelty)

MILESTONE 2: System Development

Fix implementation plan.

We prepare a clear project implementation plan that narrates your proposal in step-by step and it contains Software and OS specification. We recommend you very suitable tools/software that fit for your concept.

Tools/Plan Approval

We get the approval for implementation tool, software, programing language and finally implementation plan to start development process.

Pseudocode Description

Our source code is original since we write the code after pseudocodes, algorithm writing and mathematical equation derivations.

Develop Proposal Idea

We implement our novel idea in step-by-step process that given in implementation plan. We can help scholars in implementation.


We perform the comparison between proposed and existing schemes in both quantitative and qualitative manner since it is most crucial part of any journal paper.

Graphs, Results, Analysis Table

We evaluate and analyze the project results by plotting graphs, numerical results computation, and broader discussion of quantitative results in table.

Project Deliverables

For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.

MILESTONE 3: Paper Writing

Choosing right format.

We intend to write a paper in customized layout. If you are interesting in any specific journal, we ready to support you. Otherwise we prepare in IEEE transaction level.

Collecting Reliable Resources

Before paper writing, we collect reliable resources such as 50+ journal papers, magazines, news, encyclopedia (books), benchmark datasets, and online resources.

Writing Rough Draft

We create an outline of a paper at first and then writing under each heading and sub-headings. It consists of novel idea and resources

Proofreading & Formatting

We must proofread and formatting a paper to fix typesetting errors, and avoiding misspelled words, misplaced punctuation marks, and so on

Native English Writing

We check the communication of a paper by rewriting with native English writers who accomplish their English literature in University of Oxford.

Scrutinizing Paper Quality

We examine the paper quality by top-experts who can easily fix the issues in journal paper writing and also confirm the level of journal paper (SCI, Scopus or Normal).

Plagiarism Checking

We at phdservices.org is 100% guarantee for original journal paper writing. We never use previously published works.

MILESTONE 4: Paper Publication

Finding apt journal.

We play crucial role in this step since this is very important for scholar’s future. Our experts will help you in choosing high Impact Factor (SJR) journals for publishing.

Lay Paper to Submit

We organize your paper for journal submission, which covers the preparation of Authors Biography, Cover Letter, Highlights of Novelty, and Suggested Reviewers.

Paper Submission

We upload paper with submit all prerequisites that are required in journal. We completely remove frustration in paper publishing.

Paper Status Tracking

We track your paper status and answering the questions raise before review process and also we giving you frequent updates for your paper received from journal.

Revising Paper Precisely

When we receive decision for revising paper, we get ready to prepare the point-point response to address all reviewers query and resubmit it to catch final acceptance.

Get Accept & e-Proofing

We receive final mail for acceptance confirmation letter and editors send e-proofing and licensing to ensure the originality.

Publishing Paper

Paper published in online and we inform you with paper title, authors information, journal name volume, issue number, page number, and DOI link

MILESTONE 5: Thesis Writing

Identifying university format.

We pay special attention for your thesis writing and our 100+ thesis writers are proficient and clear in writing thesis for all university formats.

Gathering Adequate Resources

We collect primary and adequate resources for writing well-structured thesis using published research articles, 150+ reputed reference papers, writing plan, and so on.

Writing Thesis (Preliminary)

We write thesis in chapter-by-chapter without any empirical mistakes and we completely provide plagiarism-free thesis.

Skimming & Reading

Skimming involve reading the thesis and looking abstract, conclusions, sections, & sub-sections, paragraphs, sentences & words and writing thesis chorological order of papers.

Fixing Crosscutting Issues

This step is tricky when write thesis by amateurs. Proofreading and formatting is made by our world class thesis writers who avoid verbose, and brainstorming for significant writing.

Organize Thesis Chapters

We organize thesis chapters by completing the following: elaborate chapter, structuring chapters, flow of writing, citations correction, etc.

Writing Thesis (Final Version)

We attention to details of importance of thesis contribution, well-illustrated literature review, sharp and broad results and discussion and relevant applications study.

How PhDservices.org deal with significant issues ?

1. novel ideas.

Novelty is essential for a PhD degree. Our experts are bringing quality of being novel ideas in the particular research area. It can be only determined by after thorough literature search (state-of-the-art works published in IEEE, Springer, Elsevier, ACM, ScienceDirect, Inderscience, and so on). SCI and SCOPUS journals reviewers and editors will always demand “Novelty” for each publishing work. Our experts have in-depth knowledge in all major and sub-research fields to introduce New Methods and Ideas. MAKING NOVEL IDEAS IS THE ONLY WAY OF WINNING PHD.

2. Plagiarism-Free

To improve the quality and originality of works, we are strictly avoiding plagiarism since plagiarism is not allowed and acceptable for any type journals (SCI, SCI-E, or Scopus) in editorial and reviewer point of view. We have software named as “Anti-Plagiarism Software” that examines the similarity score for documents with good accuracy. We consist of various plagiarism tools like Viper, Turnitin, Students and scholars can get your work in Zero Tolerance to Plagiarism. DONT WORRY ABOUT PHD, WE WILL TAKE CARE OF EVERYTHING.

3. Confidential Info

We intended to keep your personal and technical information in secret and it is a basic worry for all scholars.


4. Publication

Most of the PhD consultancy services will end their services in Paper Writing, but our PhDservices.org is different from others by giving guarantee for both paper writing and publication in reputed journals. With our 18+ year of experience in delivering PhD services, we meet all requirements of journals (reviewers, editors, and editor-in-chief) for rapid publications. From the beginning of paper writing, we lay our smart works. PUBLICATION IS A ROOT FOR PHD DEGREE. WE LIKE A FRUIT FOR GIVING SWEET FEELING FOR ALL SCHOLARS.

5. No Duplication

After completion of your work, it does not available in our library i.e. we erased after completion of your PhD work so we avoid of giving duplicate contents for scholars. This step makes our experts to bringing new ideas, applications, methodologies and algorithms. Our work is more standard, quality and universal. Everything we make it as a new for all scholars. INNOVATION IS THE ABILITY TO SEE THE ORIGINALITY. EXPLORATION IS OUR ENGINE THAT DRIVES INNOVATION SO LET’S ALL GO EXPLORING.

Client Reviews

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phd project topics in computer science


phd project topics in computer science

Thesis and Research Topics in Computer Science

Completing a masters Thesis in computer science is the most challenging task faced by research scholars studying in universities all across the world. As computer science is one of the most vast fields opted by research scholars so finding a new thesis topic in computer science becomes more difficult. With each passing day, new and innovative developments are coming out in this era of mechanization. These developments tend to make human life much easier and better. Technology is the forerunner of this new change. Today our life is incomplete without this technology. Cell phones, laptops and all that have become an integral part of our life. Computer Science is the seed to this technical development. There are a number of good topics in computer science for project, thesis, and research for M.Tech and Ph.D. students.

In the field of academics, we need to get rid of obsolete ideas and focus on new innovative topics which are fast spreading their arms among the vast global audience. Computer Science students both in bachelors and in masters are studying the same topics and subjects from the past few years. Students don’t even have knowledge about new masters research topics. For project and thesis work also they are relying on outdated topics. Projects like school management system, library management system etc. are now out of date. Students should shift their focus to latest technologies which are highly in demand these days and future depend upon these. Here is the list of latest topics in Computer Science that you can choose and work for your project work or thesis and research:

List of few latest thesis topics in computer science is below:

Data Warehousing

Data Warehousing is the process of analyzing data for business purposes. Data warehouse store integrated data from multiple sources at a single place which can later be retrieved for making reports. The data warehouse in simple terms is a type of database different and kept isolated from organization’s run-time database. The data in the warehouse is historical data which is helpful in understanding business goals and make decisions for future prospects. It is a relatively new concept and have high growth in future. Data Warehouse provides Online Analytical Processing(OLAP) tools for the systematic and effective study of data in a multidimensional view. Data Warehouse finds its application in the following areas:

So start working on it if you have knowledge of database and data modeling.


Internet of Things(IoT)  is a concept of interconnection of various devices, a vehicle to the internet. IOT make use of actuators and sensors for transferring data to and from the devices. This technology is developed for better efficiency and accuracy apart from minimizing human interaction with the devices. The example for this is home heating in some countries when the temperature drops done through motion sensors which automatically detect the weather conditions. Another example for this is the traffic lights which changes its colors depending upon the traffic. Following are the application areas of Internet of Things(IoT):


Many people are not aware of this concept so you can choose for your project work and learn something new.

Big Data is a term to denote the large volume of data which is complex to handle. The data may be structured or unstructured. Structured data is an organized data while unstructured data is an unorganized data.  Big data  can be examined for the intuition that can give way to better decisions and schematic business moves. The definition of big data is termed in terms of three Vs. These vs are:

Application areas:


Thus you can prepare your project report or thesis report on this.

Cloud Computing

Cloud Computing is a comparatively new technology. It is an internet-based service that creates a shared pool of resources for consumers. There are three service models of  cloud computing  namely:

Characteristics of cloud computing are:

Below is the list of few latest and trending research topics in Cloud Computing :-

The common examples of cloud computing include icloud from Apple, Google-based Services like Google Drive and many more. The field is very demanding and is growing day by day. You can focus on it if you have interest in innovation.

Semantic Web

Semantic Web is also referred to as Web 3.0 and is the next big thing in the field of communication. It is standardized by World Wide Web Consortium(W3C) to promote common data formats and exchange protocols over the web. It is machine-readable information based and is built on XML technology. It is an extension to Web 2.0. In the semantic web, the information is well defined to enable better cooperation between the computers and the people. In the semantic web, the data is interlinked for better understanding. It is different from traditional data sharing technologies.

It can be a good topic for your thesis or project.

MANET stands for mobile ad hoc network. It is an infrastructure-less network with mobile devices connected wirelessly and is self-configuring. It can change locations independently and can link to other devices through a wireless connection. Following are the various types of  MANETS :

You can use various simulation tools to study the functionality and working of MANET like OPNET,  NS2 , NETSIM, NS3 etc.

In MANET there is no need of central hub to receive and send messages. Instead, the nodes directly send packets to each other.

MANET finds its applications in the following areas:


Just go for it if you have interest in the field of networking and make a project on it.

Machine Learning

It is also a relatively new concept in the field of computer science and is a technique of guiding computers to act in a certain way without programming. It makes use of certain complex algorithms to receive an input and predict an output for the same. There are three types of learning;

Machine Learning  is closely related to statistics. If you are good at statistics then you should opt this topic.

Data Mining

Data Mining is the process of identifying and establishing a relationship between large datasets for finding a solution to a problem through analysis of data. There are various tools and techniques in Data Mining which gives enterprises and organizations the ability to predict futuristic trends.  Data Mining  finds its application in various areas of research, statistics, genetics, and marketing. Following are the main techniques used in the process of Data Mining:


Advantages of Data Mining

Disadvantages of Data Mining

Artificial Intelligence is the intelligence shown by  machines  and it deals with the study and creation of intelligent systems that can think and act like human beings. In  Artificial Intelligence , intelligent agents are studied that can perceive its environment and take actions according to its surrounding environment.

Goals of Artificial Intelligence

Following are the main goals of Artificial Intelligence:

Application of Artificial Intelligence

Following are the main applications of Artificial Intelligence:

Strong AI –  It is a type of artificial intelligence system with human thinking capabilities and can find a solution to an unfamiliar task.

Weak AI –  It is a type of artificial intelligence system specifically designed for a particular task. Apple’s Siri is an example of Weak AI.

Turing Test is used to check whether a system is intelligent or not. Machine Learning is a part of Artificial Intelligence. Following are the types of agents in Artificial Intelligence systems:

Natural Language Processing –  It is a method to communicate with the intelligent systems using human language. It is required to make intelligent systems work according to your instructions. There are two processes under Natural Language Processing – Natural Language Understanding, Natural Language Generation.

Natural Language Understanding involves creating useful representations from the natural language. Natural Language Generation involves steps like Lexical Analysis, Syntactic Analysis, Semantic Analysis, Integration and Pragmatic Analysis to generate meaningful information.

Image Processing

Image Processing is another field in Computer Science and a popular topic for a thesis in Computer Science. There are two types of image processing – Analog and Digital Image Processing. Digital Image Processing is the process of performing operations on digital images using computer-based algorithms to alter its features for enhancement or for other effects. Through Image Processing, essential information can be extracted from digital images. It is an important area of research in computer science. The techniques involved in image processing include transformation, classification, pattern recognition, filtering, image restoration and various other processes and techniques.

Main purpose of Image Processing

Following are the main purposes of  image processing :

Applications of Image Processing

Following are the main applications of Image Processing:



Bioinformatics is a field that uses various computational methods and software tools to analyze the biological data. In simple words, bioinformatics is the field that uses computer programming for biological studies. It is the current topic of research in computer science and is also a good topic of choice for the thesis. This field is a combination of computer science, biology, statistics, and mathematics. It uses image and signal processing techniques to extract useful information from a large amount of data. Following are the main applications of bioinformatics:

Quantum Computing

Quantum Computing is a computing technique in which computers known as quantum computers use the laws of quantum mechanics for processing information. Quantum Computers are different from digital electronic computers in the sense that these computers use quantum bits known as qubits for processing. A lot of experiments are being conducted to build a powerful quantum computer. Once developed, these computers will be able to solve complex computational problems which cannot be solved by classical computers. Quantum is the current and the latest topic for research and thesis in computer science.

Quantum Computers work on quantum algorithms like Simon’s algorithm to solve problems. Quantum Computing finds its application in the following areas:

The list is incomplete as there are a number of topics to choose from. But these are the trending fields these days. Whether you have any presentation, thesis project or a seminar you can choose any topic from these and prepare a good report.

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MSc and PhD Research Interests

Below is a listing of research areas represented in the Computer Science Department. For some areas, their parent branch of Computer Science (such as Scientific Computing) is indicated in parentheses.

Artificial Intelligence (AI)

1. AI: Computational Linguistics & NLP

Research Topics: natural language processing (NLP), speech processing, information retrieval, machine translation, language acquisition, formal perspectives on language, cognitive modelling of language acquisition and processing, semantic change, lexical evolution, lexical composition, cross-linguistic semantic typology, applications of NLP in health and medicine, applications of NLP in the social sciences and humanities

2. AI: Computational Social Science

Research Topics: novel digital data and computational analyses for addressing societal challenges, analysis of online social networks and social media, intersection of AI and society, application of machine learning to social data, analysis of large-scale online data for social science applications, algorithmic fairness and bias

3. AI: Computer Vision

Research Topics: tracking, object recognition, 3D reconstruction, physics-based modelling of shape and appearance, computational photography, content-based image retrieval and human motion analysis

4. AI: Knowledge Representation and Reasoning

Research Topics: knowledge representation, reasoning and inference, planning and decision making, search, multi-agent systems, sequential decision-making, cognitive robotics , reasoning about knowledge, belief, acting and sensing, constraint and satisfiability reasoning

5. AI: Machine Learning

Research Topics:

Methods : deep learning, graphical models, reinforcement learning, stochastic optimization, approximate inference, structured prediction, representation learning

Theory : analysis of machine learning algorithms, convex and non-convex optimization methods, statistical learning theory

Focus in health : developing and applying machine learning methods that leverage the structure of data and problems in health including representation learning, reinforcement learning and inverse RL, prediction, risk stratification, and model interpretability

Focus in robotics : reinforcement learning, robot perception, learning and control, imitation learning, predictive models, exploration, lifelong learning, learning for self-driving cars

Focus in computer vision and graphics : image segmentation, detection

Focus in systems : cloud computing, operating systems, hardware acceleration for machine learning

6. AI: Robotics

Medical robotics, surgical robotics, continuum robotics, soft robots

Robot manipulation, kino-dynamic modelling of robots, motion planning, optimal control

Self-driving cars, mobile and field robotics, autonomous vehicles

Human-robot interaction, multi-agent systems

Computational Biology

7. Computational Biology

Research Topics: algorithms, machine learning, biomedical NLP, visualization for genomics, proteomics, and systems biology

Computational Medicine

8. Computational Medicine

 Research Topics: machine learning, human computer interaction, vision, speech and NLP for healthcare and medicine, translating computational tools to the bedside, use of mobile devices in medicine, assistive technologies: design and deployment of enabling technology to be accessible to broader groups in society

Computer Graphics

9. Computer Graphics

Computational fabrication : 3D printing, laser cutting, geometric optimizations

Computational imaging : novel 3D sensors, computational cameras, modelling real-world light transport, computer vision for photography

Geometry processing : discrete differential geometry, surface acquisition

Animation : physics-based animation, character and facial animation, biomechanical simulation

Shape modeling : sketch-based modeling and rendering

Augmented and virtual reality : interaction, perception

Computer Science & Education

10. Computer Science & Education

Computer science education : teaching and learning of computer science. Examples include: introductory programming, advanced programming, software development, visual & end-user programming for non-computer scientists, computational thinking, fostering positive attitudes and motivating diverse learners in CS.

Using computer science to enhance education : using computer science techniques to investigate educational principles and design technology for learning. Examples include: human-computer interaction design of educational technologies, adaptive and personalized learning, crowdsourcing & human computation that involves learners and instructors, educational data mining, learning analytics, artificial intelligence and statistical machine learning in education (e.g. active learning, reinforcement learning for adaptive instruction), intelligent self-improving systems & intelligent tutoring systems, randomized A/B experimentation at scale, software learning, cognitive & interactive tutorials

Data Management Systems

11. Data Management Systems

Research Topics: query processing and optimization, web data management, video and image query processing systems, applications of machine learning to processing massive data sets, approximate techniques for query processing, spatial query processing, database system internals

Human-Computer Interaction (HCI)

12. Human-Computer Interaction

Computer-Supported Cooperative Learning (CSCL)

Computer-Supported Cooperative Work (CSCW), crowdsourcing, human computation, education/learning at scale, MOOCs, interactive tutorials, software learning

Information and Communication Technology and Development (ICTD) : analysis, design and development of computing technologies for sustainable development

Information visualization : visual analytics, perception & cognition, graphical design, interface design, interaction methods

UI technologies : input/output sensors and displays, interaction methods, ubiquitous computing, AR/VR, mobile and wearable computing, room scale computing

Critical computing : critical study of contemporary computing culture, design theory

Digital fabrication : methods, materials, tools

Human-robot interaction : interface design, modelling of robots and interfaces, shared autonomy, human-robot teamwork, user modelling, intent prediction

Programming Languages & Formal Methods

13. Programming Languages & Formal Methods

Research Topics: study of programming languages, language theory, program analysis (static and dynamic), program logics and proofs of program correctness, program synthesis (automated programming), automated verification, model checking, quantitative reasoning about software systems, software safety and security, theorem proving

Quantum Computing

14. Quantum Computing

Research Topics: algorithms, cryptography, complexity, verification of quantum computers, algorithms for near-term quantum computers, quantum hamiltonian complexity, quantum machine learning, optimization, applications to physics and chemistry

Scientific Computing (SC)

15. SC: Compilers for Scientific Applications

Research Topics: domain-specific compilers, code generation, programming languages for scientific computing, autotuning, verification of numerical codes

16. SC: High-Performance Computing

Research Topics: parallel algorithms, extreme-scale scientific computing, computational science, performance modelling, compilers for scientific computing

17. SC: Numerical Analysis and Computing

Research Topics: numerical methods and analysis of ODEs and PDEs, solution of large sparse linear systems, numerical software, high performance scientific computing, scientific visualization, computational finance, medical imaging, stochastic models, effective software for systems of ODEs, DDEs and related problems, sensitivity analysis of ODE solvers

Systems & Networks (SN)

18. SN: Computer Architecture

Research Topics: architecture, hardware, compiler optimization, hardware-based acceleration, high performance computing, energy-efficient computing, hardware/software cooperation, memory systems, hardware security

19. SN: Computer Networks

Research Topics: network protocols/algorithms/systems/architecture, software-defined networking, theory of networks, online social networks, wireless networks, data centre networks, rate control, quality of service and pricing

20. SN: Systems

Research Topics: operating systems, mobile/pervasive/ubiquitous computing, virtual machines, compiler optimization, file and storage systems, reliability, cloud computing, data-intensive computing, distributed systems, datacentres

Social Networks

21. Social Networks

Research Topics: algorithms for social network analysis, graph structure of social networks, user behavior and interaction, reputation and influence, content distribution and sharing, incentive mechanisms, game theory, optimal design of online social networks, network formation and dynamics, social networks and economic theory

Software Engineering

22. Software Engineering

Software modeling and reasoning : modelling and reasoning about software, including reasoning specifically about safety and security, product line analysis, analysis of change in requirements, designs and code, analysis in/for model-driven software development

Requirements engineering : analysis and modelling of software requirements, enterprise contexts and stakeholder dependencies

Social media and collaborative work : support for team collaboration and awareness, software as a service, open source communities and distributed software development

Sustainability Informatics

23. Sustainability Informatics

Research Topics: computational models of climate change, sustainability analytics, energy efficient computing, and green IT

Theoretical Computer Science

24. Theoretical Computer Science

Research Topics: general interest in theoretical computer science including areas 25-30

25. Theory: Algorithms

Research Topics: design and analysis of algorithms and data structures, continuous and discrete optimization, randomization, approximation, fairness, algorithmic aspects of social networks

26. Theory: Computational Complexity

Research Topics: complexity of boolean functions (including circuit complexity, algebraic circuits, and quantum complexity), proof complexity, communication complexity, classes and resources (time, space, randomness)

27. Theory: Cryptography and Foundations of Privacy

Research Topics: rigorous definitions of security, cryptographic algorithms and protocols, quantum cryptography, private data analysis

28. Theory: Game Theory and Social Choice

Research Topics: equilibrium analysis, voting, resource allocation, incentives in machine learning

29. Theory: Graph Theory and Graph Algorithms

30. Theory: Distributed Computing

Research Topics: algorithms and lower bounds for distributed computing problems

Computer Science, University of Toronto


Masters Project Topics in Computer Science

                      Masters Project Topics in Computer Science is our elite academic service offered for the welfare of young scholars. Scholars often find themselves in a state of dilemma when it comes to narrowing down a single topic in Master Project topics in Computer Science. We are here to eliminate your dilemma and help you in choosing a topic that best reflects your passion and interest based on the current trends. Computer Science is a vast field, which contains multiple attractive topics. Some of the most on-demand include cloud computing, MapReduce, Hadoop, and Networking.

Apart from this, there are seventy different domains, which are available as topics in Computer Science . Computer Science is a universal field, making it a vast domain with plenty of opportunities to conduct your research.  Our wonderful professional team will guide you elaborately in choosing the topic and framing an amazing thesis based on the chosen topic.

Master Project Topics in Computer Science Online

                       Masters Project Topics in Computer Science is the philosophy we live by. It is for this reason we put extra thought and care in crafting a thesis title. Our expert is up to date as they are aware of all the on-going trends in the field of Computer Science. You can place your complete faith and trust in us as we are the world’s no.1 research institute.  More than 7000 + novice researchers in Computer Science have benefited from us. Our institute is a huge network also with branches in 120 countries.

This proves that our work is as per that of any international institute. The first and foremost step in framing a project title is to discover the interest of the researcher and his or her domain specialization. If you are unsure about your areas of specialization, contact us, we will also help you discover them.

Interests make your research interesting………

Our secret to success depends in our originality and uniqueness. To achieve this we follow the following notion:

       As we mentioned earlier, our team of experts will gift you with a novel topic due to their vast knowledge and expertise. Each domain in Computer Science is vast and full of great research scope, so we narrow our area of research based on recent trends. We never prolong the process of choosing a topic. We frame a novel and effective topic within the given time. Once the process of framing the topic is over, we also analyze the cost for completing the thesis on the framed topic. A certain domain may cost more than the other. We are also there to guide you in choosing the topic that favors your optimum cost.

Major Research Fields in Computer Science

         Apart from all the above mentioned possible topics, you can also base your research on many more areas in which you can also base your research. If this information regarding Computer Science topics is not satisfactory, you can also contact us any time. We have created an online service to solve and answer the quarries put forth by our customers. Be part of our extraordinary in wonders process of creating your dream research.

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PhD Candidate in Interaction Design and Learning Technologies


About the job

For a position as a PhD Candidate, the goal is a completed doctoral education up to an obtained doctoral degree.

Nowadays, the advances in learning technologies have introduced a new era that builds and extends their impact in learning, teaching and assessment in new and unanticipated ways. In the 21rst century digital world, STEAM (Science, Technology, Engineering, Arts and Mathematics) and especially Computer Science (CS) education appears to be more important than ever before, particularly in educating K-12 students and young people. Studying the design, evaluation and implementation of learning systems (combine the use of Learning Analytics, Augmented Reality, 3D printing, and Virtual Robotics solutions among others), dealing with the interaction in real educational settings, is demanding and challenging process. In order to explore the future of learning technologies researchers need to examine the complex interactions and invisible cognitive learning processes as they occur in learning situations using methods and data that will help us to monitor the overall learning experience in more holistic and accurate ways (e.g., multimodal data, AI/ML, other).

The successful candidate is expected to be a member of the  Learner-Computer Interaction (LCI) lab.  LCI provides an interdisciplinary playground for researchers and professionals across all areas of learning technologies, psychology, learning sciences and interaction design. 

The group leader of the position is expected to contribute on designing the conditions for engaging learning and tools for supporting learners. The objectives and the research questions of the PhD project will be developed in consultation with the supervisors during the first months of the fellowship.   Your immediate leader is the group leader of information Systems and Software engineering (ISSE). 

Duties of the position

The goal of the PhD projects is to explore learner’s (preferably with a focus on children and young adults) interaction in experiences relevant to CS education, leveraging contemporary and advanced methods. This work will build on an interdisciplinary approach, bridging the domains of learning sciences, interaction design, and learning technology.

We are seeking a highly motivated individual that wants to excel in a research environment. The successful candidate is expected to contribute to relevant projects (funded by the EU, Nationally or other) relevant to the LCI lab and group of collaborators.

Part of the main duties and responsibilities include conducting the following tasks:

Required selection criteria

The appointment is to be made in accordance with  Regulations on terms of employment for positions such as postdoctoral fellow, Phd candidate, research assistant and specialist candidate  and  Regulations concerning the degrees of Philosophiae Doctor (PhD) and Philosophiae Doctor (PhD) in artistic research at the Norwegian University of Science and Technology (NTNU)

Preferred selection criteria

Emphasis will be put on personal qualities and potential as a researcher. High importance will also be attached to personal communication and cooperation skills.

Potential successful candidate will be interviewed via digital means or in person in cases that various circumstances allow it.

Personal characteristics

Emphasis will be placed on personal and interpersonal qualities.

Salary and conditions

As a PhD candidate (code 1017) you are normally paid from gross NOK 501 200 per annum before tax, depending on qualifications and seniority. From the salary, 2% is deducted as a contribution to the Norwegian Public Service Pension Fund.

The period of employment is 4 years (with 25 % teaching duties) or 3 years (without teaching duties). 

Appointment to a PhD position requires that you are admitted to the  PhD programme in Computer Science  within three months of employment, and that you participate in an organized PhD programme during the employment period.

The engagement is to be made in accordance with the regulations in force concerning  State Employees and Civil Servants , and the acts relating to Control of the Export of Strategic Goods, Services and Technology. Candidates who by assessment of the application and attachment are seen to conflict with the criteria in the latter law will be prohibited from recruitment to NTNU.

After the appointment you must assume that there may be changes in the area of work.

It is a prerequisite you can be present at and accessible to the institution daily.

About the application

The application and supporting documentation to be used as the basis for the assessment must be in English.

Publications and other scientific work must follow the application. Please note that your application will be considered based solely on information submitted by the application deadline. You must therefore ensure that your application clearly demonstrates how your skills and experience fulfil the criteria specified above.

The application must include:

If all, or parts, of your education has been taken abroad, we also ask you to attach documentation of the scope and quality of your entire education, both bachelor's and master's education, in addition to other higher education. Description of the documentation required can be found  here . If you already have a statement from NOKUT, please attach this as well.

We will take joint work into account. If it is difficult to identify your efforts in the joint work, you must enclose a short description of your participation.

In the evaluation of which candidate is best qualified, emphasis will be placed on education, experience and personal and interpersonal qualities. Motivation, ambitions, and potential will also count in the assessment of the candidates. 

NTNU is committed to following evaluation criteria for research quality according to  The San Francisco Declaration on Research Assessment - DORA.

General information

Working at NTNU

NTNU believes that inclusion and diversity is our strength. We want to recruit people with different competencies, educational backgrounds, life experiences and perspectives to contribute to solving our social responsibilities within education and research. We will facilitate for our employees’ needs.

NTNU is working actively to increase the number of women employed in scientific positions and has a number of resources to  promote equality.  

The city of Trondheim  is a modern European city with a rich cultural scene. Trondheim is the innovation capital of Norway with a population of 200,000. The Norwegian welfare state, including healthcare, schools, kindergartens and overall equality, is probably the best of its kind in the world. Professional subsidized day-care for children is easily available. Furthermore, Trondheim offers great opportunities for education (including international schools) and possibilities to enjoy nature, culture and family life and has low crime rates and clean air quality.

As an employee at NTNU, you must at all times adhere to the changes that the development in the subject entails and the organizational changes that are adopted.

A public list of applicants with name, age, job title and municipality of residence is prepared after the application deadline. If you want to reserve yourself from entry on the public applicant list, this must be justified. Assessment will be made in accordance with  current legislation . You will be notified if the reservation is not accepted.

If you have any questions about the position, please contact Associate Professor Sofia Papavlasopoulou, email  [email protected]  . If you have any questions about the recruitment process, please contact HR, e-mail:  [email protected] .

If you think this looks interesting and in line with your qualifications, please submit your application electronically via jobbnorge.no with your CV, diplomas and certificates attached. Applications submitted elsewhere will not be considered. Upon request, you must be able to obtain certified copies of your documentation.  

Application deadline: 15.03.23


NTNU - knowledge for a better world

The Norwegian University of Science and Technology (NTNU) creates knowledge for a better world and solutions that can change everyday life.

Department of Computer Science 

We are the leading academic IT environment in Norway, and offer a wide range of theoretical and applied IT programmes of study at all levels. Our subject areas include hardware, algorithms, visual computing, AI, databases, software engineering, information systems, learning technology, HCI, CSCW, IT operations and applied data processing. The Department has groups in both Trondheim and Gjøvik. The  Department of Computer Science  is one of seven departments in the  Faculty of Information Technology and Electrical Engineering  .

Deadline  15th March 2023 Employer  NTNU - Norwegian University of Science and Technology Municipality  Trondheim Scope  Fulltime Duration Temporary Place of service Campus Gløshaugen

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