cambridge computer science phd students

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PhD in Scientific Computing

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Information on how to enter our PhD programme

A common route for admission into our PhD programme is via the Centre’s MPhil programme in Scientific Computing. The MPhil is offered by the University of Cambridge as a full-time course and introduces students to research skills and specialist knowledge. Covering topics of high-performance scientific computing and advanced numerical methods and techniques, it produces graduates with rigorous research and analytical skills, who are formidably well-equipped to proceed to doctoral research or directly into employment in industry.

List of the groups who offer PhD positions in Scientific Computing and its applications

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Cambridge centre for data-driven discovery, currently advertised phd studentships.

The majority of current PhD studentships are listed on the University's Jobs site
For a full list of departments and faculties at the University, visit this page where you can learn more about the research interests within each department
To find academics you might like to work with, use our directory

Graduate Admissions

The Graduate Admissions office provides a range of information on postgraduate programmes at Cambridge, along with a step-by-step guide to the application process. It is advisable to start researching funding opportunities at least a year before your course begins.

MPhil and PhD course relevant to data science - from across University of Cambridge

Please visit the relevant pages and contact the relevant education provider if you have queries. You should pay particular attention to the entry requirements and guidance for applicants there.

MPhil in Machine Learning and Machine Intelligence - an eleven month full-time programme offered by the Machine Learning Group, the Speech Group, and the Computer Vision and Robotics Group in the Cambridge University Department of Engineering. The course aims to teach the state-of-the-art in machine learning, speech and language processing, and computer vision; to give students the skills and expertise necessary to take leading roles in industry and to equip them with the research skills necessary for doctoral study at Cambridge and other universities.

PhD programme in Advanced Machine Learning - The Machine Learning Group is based in the Department of Engineering, and encourages applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific applications.

Cambridge Centre for AI in Medicine - Cambridge Centre for AI in Medicine (CCAIM) is a multi-disciplinary centre established by the University of Cambridge in 2020 to develop pioneering AI machine learning (ML) technologies that will transform biomedical science, medicine and healthcare. PhD studentships are oten available, please check their website for details.

SynTech Centre for Doctoral Training - EPSRC Centre for Doctoral Training in Next Generation Synthetic Chemistry Enabled by Digital Molecular Technologies. An interdisciplinary cohort-driven programme to produce the next generation of molecule making scientists by combining Synthetic Chemistry, Chemical Engineering, Engineering, Machine Learning and Artificial Intelligence.

Advanced Computer Science MPhil - The MPhil in Advanced Computer Science (the ACS) is designed to prepare students for doctoral research, whether at Cambridge or elsewhere. Typical applicants will have undertaken a first degree in computer science or an equivalent subject, and will be expected to be familiar with basic concepts and practices. The ACS is a nine–month course which starts in early October and finishes on 30 June. It covers advanced material in both theoretical and practical areas as well as instilling the elements of research practice.

Application of Artificial Intelligence to the study of Environmental Risks MRes and PhD - The UKRI Centre for Doctoral Training in the Application of Artificial Intelligence to the study of Environmental Risks (AI4ER) trains researchers (through several multidisciplinary cohorts) to be uniquely equipped to develop and apply leading-edge computational approaches to address critical global environmental challenges by exploiting vast, diverse and often currently untapped environmental data sets. Embedded in the outstanding research environments of the University of Cambridge and the British Antarctic Survey (BAS), the AI4ER CDT addresses problems that are relevant to building resilience to environmental hazards and managing environmental change .

Postgraduate Study in Mathematics - Various postgraduate courses of a mathematical nature are available at the University of Cambridge, including both taught courses and research degrees.

Mathematics of Information PhD - This cutting-edge training Centre in the Mathematics of Information produces a new generation of leaders in the theory and practice of modern data science, with an emphasis on the mathematical underpinnings of this new scientific field. The Cambridge Mathematics of Information (CMI) PhD is a four-year course leading to a single PhD thesis.

Cambridge Computational Biology Institute MPhil and PhD - The MPhil in Computational Biology course is aimed at introducing students in the biological, mathematical and physical sciences to quantitative aspects of modern biology and medicine, including bioinformatics. The course has been developed by the Cambridge Computational Biology Institute and is run by the Department of Applied Mathematics and Theoretical Physics at the Centre for Mathematical Sciences (CMS).

Centre for Scientific Computing MPhil and PhD - The MPhil programme on Scientific Computing is offered by the University of Cambridge as a full-time course which aims to provide education of the highest quality at Master’s level. A common route for admission into our PhD programme is via the Centre’s MPhil programme in Scientific Computing.

Part III Mathematics - Part III is a 9 month taught masters course in mathematics. It is an excellent preparation for mathematical research and it is also a valuable course in mathematics and in its applications for those who want further training before taking posts in industry, teaching, or research establishments. Students admitted from outside Cambridge to Part III study towards the Master of Advanced Study (MASt). Students continuing from the Cambridge Tripos for a fourth year, study towards the Master of Mathematics (MMath). The requirements and course structure for Part III are the same for all students irrespective of whether they are studying for the MASt or MMath degree. There are over 200 Part III (MASt and MMath) students each year; almost all are in their fourth or fifth year of university studies.

School of Clinical Medicine Graduate Training Office - Prospective students interested in pursuing a graduate degree course in a subject area related to clinical medicine at the University of Cambridge should consult the School’s individual departmental websites for detailed information about the courses which they run and the University’s Graduate Admissions website for information on the application process and on funding opportunities.

Centre for Doctoral Training in Data, Risk And Environmental Analytical Methods - The CDT embraces a wide range of world-leading Doctoral research in the area of Big Data and Environmental Risk Mitigation. The CDT research underway seeks to utilise emerging technologies, techniques and tools, to more accurately monitor the environment, enabling cutting edge research. To provide end-users with more integrated information at improved temporal and spatial resolutions to deliver solutions to environmental challenges (both acute and long- term). Funded by NERC (the Natural Environment Research Council, NERC Ref: NE/M009009/1), the DREAM (Data, Risk and Environmental Analytical Methods) consortium is made up of Cranfield, Newcastle, Cambridge and Birmingham universities.

Centre for Doctoral Training in Data Intensive Science - The Cambridge CDT in Data Intensive Science is an innovative, interdisciplinary centre, distributed between the Department of Physics (Cavendish Laboratory), Department of Applied Mathematics and Theoretical Physics (DAMTP), Department of Pure Mathematics and Mathematical Statistics (DPMMS) and the Institute of Astronomy (IoA).

MPhil in Data Intensive Science - This course aims to take science graduates and to prepare them for data intensive research careers by providing advanced training in three key areas – Statistical Analysis, Machine Learning, and Research Computing – and their application to current research frontiers.

Cambridge Digital Humanities - The MPhil provides the opportunity to specialise in a chosen subject area as well as an advanced level introduction to DH approaches, methods and theory. The course provides critical and practical literacy, the chance to advance an extant specialization by re-contextualizing it in relation to advanced theoretical work, and the chance to develop as a DH scholar.

The Cambridge Centre for Data-Driven Discovery (C2D3) brings together researchers and expertise from across the academic departments and industry to drive research into the analysis, understanding and use of data science and AI. C2D3 is an Interdisciplinary Research Centre at the University of Cambridge.

Supports and connects the growing data science and AI research community
Builds research capacity in data science and AI to tackle complex issues
Drives new research challenges through collaborative research projects
Promotes and provides opportunities for knowledge transfer
Identifies and provides training courses for students, academics, industry and the third sector
Serves as a gateway for external organisations

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MPhil in Advanced Computer Science

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Course closed:

Advanced Computer Science is no longer accepting new applications.

The aim of the course is to provide preparation appropriate for undertaking a PhD programme in computer science. Students select five taught modules from a wide range of advanced topics in computer science from networking and systems measurements to category theory, and topics in natural language processing. Additionally, students take a mandatory, ungraded course in research skills which includes core and optional topics.

Students also undertake a research project over two terms and submit a project report in early June. Research topic selection and planning occurs in the first term and the work is undertaken in subsequent terms. The taught modules are delivered in a range of styles. For example, there are traditional lecture courses, lecture courses with associated practical classes, reading clubs, and seminar style modules.

The course aims to:

give students, with relevant experience at a first-degree level, the opportunity to carry out directed research in the discipline;
give students the opportunity to acquire or develop skills and expertise relevant to their research interests;
provide preparation appropriate for undertaking a PhD programme in computer science;
provide the Faculty with an extended period in which to train students and then to judge the suitability of students for PhD study; and
offer a qualification that is valuable and highly marketable in its own right that equips its graduates with the computer science related research skills and expertise to play leading roles in industrial and public-sector research.

Learning Outcomes

By the end of the programme, the students will have:

a comprehensive understanding of techniques, and a thorough knowledge of the literature, applicable to their chosen area;
demonstrated some originality in the application of knowledge, together with an understanding of how research and enquiry are used to create and interpret knowledge in their chosen area;
shown abilities in the critical evaluation of current research and research techniques and methodologies; and
demonstrated some self-direction and originality in tackling and solving problems, and acted autonomously in the planning and implementation of research.

The minimum requirement for continuation to the PhD programme in computer science is that MPhil students achieve an overall pass in the taught modules and, separately, the project. The pass mark is 60 per cent; however, higher minimum requirements may be set at the discretion of the Department and Degree Committee. Continuation to the PhD degree is dependent on the approval of the Department and Degree Committee.

The Postgraduate Virtual Open Day usually takes place at the end of October. It’s a great opportunity to ask questions to admissions staff and academics, explore the Colleges virtually, and to find out more about courses, the application process and funding opportunities. Visit the Postgraduate Open Day page for more details.

See further the Postgraduate Admissions Events pages for other events relating to Postgraduate study, including study fairs, visits and international events.

Key Information

9 months full-time, study mode : taught, master of philosophy, department of computer science and technology, course - related enquiries, application - related enquiries, course on department website, dates and deadlines:, michaelmas 2024 (closed).

Some courses can close early. See the Deadlines page for guidance on when to apply.

Funding Deadlines

These deadlines apply to applications for courses starting in Michaelmas 2024, Lent 2025 and Easter 2025.

Similar Courses

Computer Science PhD
Computation, Cognition and Language PhD
Machine Learning and Machine Intelligence MPhil
Linguistics: Theoretical and Applied Linguistics PhD

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Computer Science

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Computer Science is an area of study within the Harvard John A. Paulson School of Engineering and Applied Sciences. Prospective students apply through Harvard Griffin GSAS; in the online application, select “Engineering and Applied Sciences” as your program choice and select "PhD Computer Science" in the Area of Study menu.

In the Computer Science program, you will learn both the fundamentals of computation and computation’s interaction with the world. Your work will involve a wide range of areas including theoretical computer science, artificial intelligence and machine learning, economics and computer science, privacy and security, data-management systems, intelligent interfaces, operating systems, computer graphics, computational linguistics, robotics, networks, architectures, program languages, and visualization.

You will be involved with researchers in several interdisciplinary initiatives across the University, such as the Center for Research on Computation and Society, the Institute for Applied Computational Science, the Data Science Initiative, and the Berkman Klein Center for Internet and Society.

Examples of projects current and past students have worked on include leveraging machine learning to solve real-world sequential decision-making problems and using artificial intelligence to help conservation and anti-poaching efforts around the world.

Graduates of the program have gone on to a range of careers in industry in companies like Riot Games as game director and Lead Scientist at Raytheon. Others have positions in academia at University of Pittsburgh, Columbia, and Stony Brook.

Standardized Tests

GRE General: Not Accepted

APPLICATION DEADLINE

Questions about the program.

Internal wiki

PhD Programme in Advanced Machine Learning

The Cambridge Machine Learning Group (MLG) runs a PhD programme in Advanced Machine Learning. The supervisors are Jose Miguel Hernandez-Lobato , Carl Rasmussen , Richard E. Turner , Adrian Weller , Hong Ge and David Krueger . Zoubin Ghahramani is currently on academic leave and not accepting new students at this time.

We encourage applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific applications. There are no additional restrictions on the topic of the PhD, but for further information on our current research areas, please consult our webpages at http://mlg.eng.cam.ac.uk .

The typical duration of the PhD will be four years.

Applicants must formally apply through the Applicant Portal at the University of Cambridge by the deadline, indicating “PhD in Engineering” as the course (supervisor Hernandez-Lobato, Rasmussen, Turner, Weller, Ge and/or Krueger). Applicants who want to apply for University funding need to reply ‘Yes’ to the question ‘Apply for Cambridge Scholarships’. See http://www.admin.cam.ac.uk/students/gradadmissions/prospec/apply/deadlines.html for details. Note that applications will not be complete until all the required material has been uploaded (including reference letters), and we will not be able to see any applications until that happens.

Gates funding applicants (US or other overseas) need to fill out the dedicated Gates Cambridge Scholarships section later on the form which is sent on to the administrators of Gates funding.

Deadline for PhD Application: noon 5 December, 2023

Applications from outstanding individuals may be considered after this time, but applying later may adversely impact your chances for both admission and funding.

FURTHER INFORMATION ABOUT COMPLETING THE ADMISSIONS FORMS:

The Machine Learning Group is based in the Department of Engineering, not Computer Science.

We will assess your application on three criteria:

1 Academic performance (ensure evidence for strong academic achievement, e.g. position in year, awards, etc.) 2 references (clearly your references will need to be strong; they should also mention evidence of excellence as quotes will be drawn from them) 3 research (detail your research experience, especially that which relates to machine learning)

You will also need to put together a research proposal. We do not offer individual support for this. It is part of the application assessment, i.e. ascertaining whether you can write about a research area in a sensible way and pose interesting questions. It is not a commitment to what you will work on during your PhD. Most often PhD topics crystallise over the first year. The research proposal should be about 2 pages long and can be attached to your application (you can indicate that your proposal is attached in the 1500 character count Research Summary box). This aspect of the application does not carry a huge amount of weight so do not spend a large amount of time on it. Please also attach a recent CV to your application too.

INFORMATION ABOUT THE CAMBRIDGE-TUEBINGEN PROGRAMME:

We also offer a small number of PhDs on the Cambridge-Tuebingen programme. This stream is for specific candidates whose research interests are well-matched to both the machine learning group in Cambridge and the MPI for Intelligent Systems in Tuebingen. For more information about the Cambridge-Tuebingen programme and how to apply see here . IMPORTANT: remember to download your application form before you submit so that you can send a copy to the administrators in Tuebingen directly . Note that the application deadline for the Cambridge-Tuebingen programme is noon, 5th December, 2023, CET.

What background do I need?

An ideal background is a top undergraduate or Masters degree in Mathematics, Physics, Computer Science, or Electrical Engineering. You should be both very strong mathematically and have an intuitive and practical grasp of computation. Successful applicants often have research experience in statistical machine learning. Shortlisted applicants are interviewed.

Do you have funding?

There are a number of funding sources at Cambridge University for PhD students, including for international students. All our students receive partial or full funding for the full three years of the PhD. We do not give preference to “self-funded” students. To be eligible for funding it is important to apply early (see https://www.graduate.study.cam.ac.uk/finance/funding – current deadlines are 10 October for US students, and 1 December for others). Also make sure you tick the box on the application saying you wish to be considered for funding!

If you are applying to the Cambridge-Tuebingen programme, note that this source of funding will not be listed as one of the official funding sources, but if you apply to this programme, please tick the other possible sources of funding if you want to maximise your chances of getting funding from Cambridge.

What is my likelihood of being admitted?

Because we receive so many applications, unfortunately we can’t admit many excellent candidates, even some who have funding. Successful applicants tend to be among the very top students at their institution, have very strong mathematics backgrounds, and references, and have some research experience in statistical machine learning.

Do I have to contact one of the faculty members first or can I apply formally directly?

It is not necessary, but if you have doubts about whether your background is suitable for the programme, or if you have questions about the group, you are welcome to contact one of the faculty members directly. Due to their high email volume you may not receive an immediate response but they will endeavour to get back to you as quickly as possible. It is important to make your official application to Graduate Admissions at Cambridge before the funding deadlines, even if you don’t hear back from us; otherwise we may not be able to consider you.

Do you take Masters students, or part-time PhD students?

We generally don’t admit students for a part-time PhD. We also don’t usually admit students just for a pure-research Masters in machine learning , except for specific programs such as the Churchill and Marshall scholarships. However, please do note that we run a one-year taught Master’s Programme: The MPhil in Machine Learning, and Machine Intelligence . You are welcome to apply directly to this.

What Department / course should I indicate on my application form?

This machine learning group is in the Department of Engineering. The degree you would be applying for is a PhD in Engineering (not Computer Science or Statistics).

How long does a PhD take?

A typical PhD from our group takes 3-4 years. The first year requires students to pass some courses and submit a first-year research report. Students must submit their PhD before the 4th year.

What research topics do you have projects on?

We don’t generally pre-specify projects for students. We prefer to find a research area that suits the student. For a sample of our research, you can check group members’ personal pages or our research publications page.

What are the career prospects for PhD students from your group?

Students and postdocs from the group have moved on to excellent positions both in academia and industry. Have a look at our list of recent alumni on the Machine Learning group webpage . Research expertise in machine learning is in very high demand these days.

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Computer Science, BA (Hons) and MEng

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Computer Science at Cambridge

Computer Science at Cambridge brings together disciplines including mathematics, engineering, the natural sciences, psychology and linguistics.

Study modern computer science, along with the underlying theory and foundations in economics, law and business.

Here at Cambridge, we pioneered computer science and we continue to lead its development today.

Our links to Computing go back to the 1930s when Alan Turing developed the theoretical foundations for computation. We’ve been at the forefront of Computer Science research ever since.

This is a broad and deep course that covers all aspects of modern computer science.

We have 3 and 4 year course options:

the 3-year course is a BA honours degree
the 4-year course includes a Masters, leading to a BA and Master of Engineering (MEng) degree

Whichever option you choose, you will develop practical skills in:

programming, in various languages such as OCaml, Java, C/C++ and Prolog
hardware systems, such as chip design

Teaching and facilities

We are the oldest Computer Science department in the world – our Computer Lab has been at the forefront of research in Computer Science ever since its inception, in 1970.

We offer a learning environment that is creative, stimulating, modern and entrepreneurial. You will be taught by pioneers and leading researchers in this fast-moving field.

You'll also take part in group projects which will be presented to external companies. Find out more about how Computer Science at Cambridge can support your future career.

The Department of Computer Science and Technology is packed with the latest technology. Our facilities include:

advanced lecture theatres
dedicated practical rooms

Our West Cambridge site, home of the Computer Laboratory, offers:

a fantastic environment for both study and relaxation
a large library stocked with the latest Computer Science publications
big and comfortable lecture theatres
a great café

At Cambridge, you'll also have access to the impressive Cambridge University Library, one of the world’s oldest university libraries.

Course costs

When you go to university, you’ll need to consider two main costs – your tuition fees and your living costs (sometimes referred to as maintenance costs).

Your living costs will include costs related to your studies that are not covered by your tuition fees. There are some general study costs that will apply for all students – you can find details of these costs here .

Other additional costs for Computer Science are detailed below. If you have any queries about resources/materials, please contact the Department.

Laptop specification: £800 for a modern entry-level laptop is sufficient, but we recommend at least half the main drive is dedicated to a bootable Linux system, such as Ubuntu.
University approved scientific calculator: please see the Department website for details.

You don't have to buy your own copies of textbooks, but it's strongly recommended. The number of textbooks you need depends on the course options you’ve chosen. The costs below are an estimate of how much you can expect to spend each year if you do purchase your own copies.

Year 1: Estimated cost of core texts £150.
Years 2, 3 and 4: Estimated cost of core texts £150 to £250 per year.

Your future career

There are more than 1,000 specialist computing and advanced technology companies and commercial laboratories in the Cambridge area, known as ‘Silicon Fen'.

A number of local firms and start-ups support our teaching and employ our graduates, in areas from chip design to mathematical modelling and AI.

As a graduate, you’ll have knowledge and skills that embody principles which will outlast today’s technology. This makes you highly sought after by industry and commerce alike.

Many of our graduates go on to work as:

programmers
software development professionals

Other graduates decide to pursue:

further study
careers in teaching and research

Many have also founded companies, or gained employment in:

the games industry
communications

Teaching is provided through lectures, practical classes and small-group supervisions.

In your first year you will typically have 20 hours of teaching each week, including up to 12 lectures and practical classes.

In your first and second year you will be assessed through 3-hour examinations, taken in the final term of each year.

In your third year you will be assessed through coursework and 3-hour examinations.

Practical work is undertaken and assessed in all years of the degree programme.

You won't usually be able to resit any of your exams.

Year 1 (Part IA)

You take 4 papers, including 3 compulsory Computer Science papers, covering topics such as:

foundations of computer science, taught in OCaml
Java and object-oriented programming
operating systems
digital electronics
interaction design
machine learning

You will also take a Mathematics paper, from the first year of the Natural Sciences course.

Year 2 (Part IB)

You take 4 papers, spanning core topics:

theory – including logic and proof, computation theory
systems – including computer architecture, computer networking
programming – including compiler construction, programming in C/C++
human aspects – including Human Interaction design, Artificial Intelligence

You also undertake a group project, which reflects current industrial practice.

Year 3 (Part II)

You choose from a large selection of topics which allows you to concentrate on an area of interest to you, such as:

computer architecture
applications (including bioinformatics and natural language processing)

New topics inspired by current research interests include computer architecture, data science and robotics.

You will also work on a substantial project that demonstrates your computer science skills, and write a 10,000 to 12,000 word dissertation on it.

Projects are often connected with current Cambridge research, and many utilise cutting-edge technology.

Year 4 (Part III, optional Masters)

The fourth year is designed for students considering a career in academic or industrial research.

explore issues at the very forefront of computer science
undertake a substantial research project

Progression to fourth year depends on how well you do in your third year exams.

If you successfully complete the fourth year, you’ll get the MEng qualification, as well as the BA degree which you get at the end of the third year.

For further information about this course and the papers you can take see the Faculty of Computer Science and Technology website .

Changing course

It’s really important to think carefully about which course you want to study before you apply.

In rare cases, it may be possible to change course once you’ve joined the University. You will usually have to get agreement from your College and the relevant departments. It’s not guaranteed that your course change will be approved.

You might also have to:

take part in an interview
complete an admissions test
produce some written work
achieve a particular grade in your current studies
do some catch-up work
start your new course from the beginning

For more information visit the Faculty website .

You can also apply to change to:

Management Studies at the Judge Business School

You can't apply to this course until you're at Cambridge. You would usually apply when you have completed 1 year or more of your original Cambridge course.

You should contact your College’s Admissions Office if you’re thinking of changing your course. They will be able to give you advice and explain how changing courses works.

Minimum offer level

A level: A*A*A IB: 41-42 points, with 776 at Higher Level Other qualifications : Check which other qualifications we accept .

Subject requirements

To apply to any of our Colleges for Computer Science, you will need A levels/IB Higher Levels (or the equivalent) in:

Mathematics
Further Mathematics to AS or A level if your school offers it. Please see the further guidance below.

If you’re studying IB, we ask for Analysis and Approaches for this course. If this isn’t an option at your school, please contact the College you wish to apply to for advice.

If you’re applying to Churchill, Downing or Lucy Cavendish, you will also need a third science subject at A level/IB Higher Level. If you apply to Christ's College you must have Further Mathematics A level.

Colleges will require A*/7 in Mathematics or Further Mathematics. Colleges may also require an A*/7 in specific subjects as part of your offer.

If applying to Churchill, you will need to achieve:

A Level: A* in Further Mathematics, if available at your school/college (otherwise A* in Mathematics), and A* in at least one of Chemistry, Computer Science, and Physics
IB: 7 in Higher Level Mathematics and 7 in at least one of Higher Level Chemistry, Computer Science, and Physics

Further Mathematics A level and additional maths

If your school offers Further Mathematics to AS or A level, you should take it.

Additional mathematics is helpful and all candidates are strongly encouraged to take up opportunities to develop their skills, such as by participating in olympiads or accessing the online resources in the Advanced Mathematics Support Programme .

What Computer Science students have studied

Most Computer Science students (who had studied A levels and started at Cambridge in 2017-19) achieved at least A*A*A* (81% of entrants).

All of these students studied Mathematics and most also took:

Further Mathematics (96%)
Physics (85%)
Computing (59%)

The majority of students who studied IB achieved at least 43 points overall.

Check our advice on choosing your high school subjects . You should also check if there are any required subjects for your course when you apply.

Admission assessment

All applicants for Computer Science for 2025 entry are required to take the Test of Mathematics for University Admission (TMUA) at an authorised assessment centre. You must register in advance for this test.

Please see the admissions test page for more information.

Check the TMUA page for further details and example papers.

Submitted work

You won't usually be asked to submit examples of written work. You may be asked to do some reading prior to your interview, but if this is required the College will provide full details in your interview invitation.

Offers above the minimum requirement

The minimum offer level and subject requirements outline the minimum you'll usually need to achieve to get an offer from Cambridge.

In some cases, you'll get a higher or more challenging offer. Colleges set higher offer requirements for a range of reasons. If you'd like to find out more about why we do this, check the information about offers above the minimum requirement on the entry requirements page.

Some Colleges usually make offers above the minimum offer level. Find out more on our qualifications page .

All undergraduate admissions decisions are the responsibility of the Cambridge Colleges. Please contact the relevant College admissions office if you have any queries.

Discover your department or faculty

Visit the Department of Computer Science and Technology website - The Department of Computer Science and Technology website has more information about this course, facilities, people and research.

Explore our Colleges

Find out how Colleges work - A College is where you’ll live, eat and socialise. It’s also where you’ll have teaching in a small group, known as supervisions.
How to choose a Cambridge College that's right for you - If you think you know which course you’d like to study, it’s time to choose a College.

Visit us on open day

Book an open day - Get a feel for the city and the University.
Find an event - We offer a range of events where you can find out more about Cambridge, Colleges, and your course. Many of our events have hybrid options so you can join us virtually.

Find out how to apply

Find out how to apply and how our admissions processes work - Our admissions process is slightly different to other universities. We’ve put together a handy guide to tell you everything you need to know about applying to study at Cambridge.
Improve your application - Supercurricular activities are a great way to engage with your chosen subject outside of school or college.

Discover Uni data

Contextual information.

Discover Uni allows you to compare information about individual courses at different higher education institutions. This can be a useful method of considering your options and what course may suit you best.

However, please note that superficially similar courses often have very different structures and objectives, and that the teaching, support and learning environment that best suits you can only be determined by identifying your own interests, needs, expectations and goals, and comparing them with detailed institution- and course-specific information.

We recommend that you look thoroughly at the course and University information contained on these webpages and consider coming to visit us on an Open Day , rather than relying solely on statistical comparison.

You may find the following notes helpful when considering information presented by Discover Uni.

Discover Uni relies on superficially similar courses being coded in the same way. Whilst this works on one level, it may lead to some anomalies. For example, Music courses and Music Technology courses can have exactly the same code despite being very different programmes with quite distinct educational and career outcomes. Any course which combines several disciplines (as many courses at Cambridge do) tends to be compared nationally with courses in just one of those disciplines, and in such cases the Discover Uni comparison may not be an accurate or fair reflection of the reality of either. For example, you may find that when considering a degree which embraces a range of disciplines such as biology, physics, chemistry and geology (for instance, Natural Sciences at Cambridge), the comparison provided is with courses at other institutions that primarily focus on just one (or a smaller combination) of those subjects.You may therefore find that not all elements of the Cambridge degree are represented in the Discover Uni data.
Some contextual data linked from other surveys, such as the National Student Survey (NSS) or the Destination of Leavers in Higher Education (DLHE), may not be available or may be aggregated across several courses or several years due to small sample sizes. When using the data to inform your course choice, it is important to ensure you understand how it has been processed prior to its presentation. Discover Uni offers some explanatory information about how the contextual data is collated, and how it may be used, which you can view here: https://discoveruni.gov.uk/about-our-data/ .
Discover Uni draws on national data to provide average salaries and employment/continuation data. Whilst starting salaries can be a useful measure, they do not give any sense of career trajectory or take account of the voluntary/low paid work that many graduates undertake initially in order to gain valuable experience necessary/advantageous for later career progression. Discover Uni is currently piloting use of the Longitudinal Education Outcomes (LEO) data to demonstrate possible career progression; it is important to note that this is experimental and its use may be modified as it embeds.

The above list is not exhaustive and there may be other important factors that are relevant to the choices that you are making, but we hope that this will be a useful starting point to help you delve deeper than the face value of the Discover Uni data.

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Spotlight on...

10 colleges and universities shaping the future of cybersecurity education

Institutions featured on this list often provide undergraduate and graduate degrees, courses, as well as certificate programs tailored to meet the growing demand for cybersecurity professionals in various industries.

Some notable colleges and universities renowned for their cybersecurity programs and courses include:

Carnegie Mellon University (USA)

Information Networking Institute (INI)

The Information Networking Institute (INI) at Carnegie Mellon University (CMU) educates and develops engineers through technical, interdisciplinary master’s degree programs in information networking, security and mobile and IoT engineering that incorporate business and policy perspectives.

Program : Master of Science in Information Security (MSIS)

Georgia Institute of Technology (USA)

Institute for Information Security & Privacy (IISP)

The Georgia Institute of Technology’s Institute for Information Security & Privacy (IISP) is a research institution dedicated to advancing cybersecurity and privacy technologies. Established within Georgia Tech, the IISP serves as a focal point for interdisciplinary research, education, and collaboration in the field of information security and privacy.

Program : Master of Science in Cybersecurity

Massachusetts Institute of Technology (USA)

MIT Department of Electrical Engineering and Computer Science

A joint venture between the Schwarzman College of Computing and the School of Engineering, EECS is grounded in three overlapping sub-units: electrical engineering (EE), computer science (CS), and artificial intelligence and decision-making (AI+D).

Computer Science and Engineering
Artificial Intelligence and Decision Making

Stanford University (USA)

Cyber Policy Center and Computer Science Department

The Cyber Policy Center brings together researchers across the Stanford campus to solve the biggest issues in cybersecurity, governance and the future of work.

Global Digital Policy Incubator
The Program on Platform Regulation
Geopolitics, Technology, and Governance

SANS Technology Institute (USA)

An independent subsidiary of SANS, the SANS Technology Institute offers graduate programs (master’s degree and graduate certificates) that develop technically-adept leaders and undergraduate programs (bachelor’s degree and undergraduate certificate) for people who want to enter the cybersecurity field.

Program : Cybersecurity Master’s Degree

University of California, Berkeley (USA)

School of Information

The School of Information is a graduate research and education community committed to expanding access to information and to improving its usability, reliability, and credibility while preserving security and privacy. This requires the insights of scholars from diverse fields — information and computer science, design, social sciences, management, law, and policy.

Program : Master of Information and Cybersecurity (MICS)

University of Cambridge (UK)

Department of Computer Science and Technology

The Department of Computer Science and Technology (formerly known as the Computer Laboratory) is the academic department within the University of Cambridge that encompasses computer science, along with many aspects of technology, engineering and mathematics.

Cybersecurity
Software and Security Engineering

University of Oxford (UK)

Global Cyber Security Capacity Centre (GCSCC)

The Global Cyber Security Capacity Centre (GCSCC) is an international centre for research on efficient and effective cybersecurity capacity-building, promoting an increase in the scale, pace, quality and impact of cybersecurity capacity-building initiatives across the world.

Course : MSc in Software and Systems Security

Technische Universität Darmstadt (Germany)

Department of Computer Science

The scientists of the Department of Computer Science combine their diverse research activities in three main research areas:

Artificial Intelligence
Complex Networked Systems
Cybersecurity & Privacy

Program : Master’s degree program IT Security

Tel Aviv University (Israel)

Research is a cornerstone of Tel Aviv University’s mission, with its scholars making discoveries in fields ranging from biotechnology and cybersecurity to archaeology and social sciences.

Cyber Security Program
Cyber Politics & Government

Fill out the form to get your copy of this whitepaper and find out what it takes to join a growing industry:

I have read and agree to the Privacy Policy and would like to be updated on ISC2 certifications, educational resources, and offers.
Carnegie Mellon University
cybersecurity
skill development
University of Cambridge

Computer science could become required to graduate in Louisiana

Computer science classes might be required for Louisiana high school graduates starting in the 2027-28 school year.

House Bill 264 , authored by Democrat Rep. Jason Hughes from New Orleans, passed without objection Wednesday out of the Senate Education Committee. The bill adds computer science requirements for every path to a high school diploma at Louisiana public schools.

Computer science would also be required to receive any award from Taylor Opportunity Program for Students . The TOPS Opportunity, Performance and Honors awards would require students to take one credit of computer science as part of their math or science curriculum or in place of a foreign language.

For TOPS Tech, students can take computer science as one of their math or science courses or as one of their electives.

The option for using computer science to complete graduation requirements would partially begin in the 2027-28 school year.

For those graduating in 2028 and pursuing TOPS Opportunity, Performance, and Honors awards, computer science can only be used in place of the foreign language requirement. The TOPS Tech pathway does not allow for students graduating in 2028 to use computer science to fulfill any of the existing requirements.

Two years ago, then-Sen. Sharon Hewitt, R-Slidell, gained approval for a bill that allowed students to count two credits of computer science instead of a foreign language for TOPS eligibility.

Hughes’ bill now heads to the Senate floor.

— The Louisiana Illuminator is an independent, nonprofit, nonpartisan news organization driven by its mission to cast light on how decisions are made in Baton Rouge and how they affect the lives of everyday Louisianians, particularly those who are poor or otherwise marginalized.

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Principal lecturers: Prof Frank Stajano , Dr Damon Wischik Taken by: Part IA CST Hours: 24 Suggested hours of supervisions: 6 This course is a prerequisite for: Advanced Algorithms , Artificial Intelligence , Complexity Theory , Prolog Past exam questions

The aim of this course is to provide an introduction to computer algorithms and data structures, with an emphasis on foundational material.

Sorting. Review of complexity and O-notation. Trivial sorting algorithms of quadratic complexity. Review of merge sort and quicksort, understanding their memory behaviour on statically allocated arrays. Heapsort. Stability. Other sorting methods including sorting in linear time. Median and order statistics. [Ref: CLRS3 chapters 1, 2, 3, 6, 7, 8, 9] [about 4 lectures]
Strategies for algorithm design. Dynamic programming, divide and conquer, greedy algorithms and other useful paradigms. [Ref: CLRS3 chapters 4, 15, 16] [about 3 lectures]
Data structures. Elementary data structures: pointers, objects, stacks, queues, lists, trees. Binary search trees. Red-black trees. B-trees. Hash tables. Priority queues and heaps. [Ref: CLRS3 chapters 6, 10, 11, 12, 13, 18] [about 5 lectures]
Graph algorithms. Graph representations. Breadth-first and depth-first search. Topological sort. Minimum spanning tree. Kruskal and Prim algorithms. Single-source shortest paths: Bellman-Ford and Dijkstra algorithms. All-pairs shortest paths: matrix multiplication and Johnson’s algorithms. Maximum flow: Ford-Fulkerson method, Max-Flow Min-Cut Theorem. Matchings in bipartite graphs. [Ref: CLRS3 chapters 22, 23, 24, 25, 26] [about 7 lectures]
Advanced data structures. Binomial heap. Amortized analysis: aggregate analysis, potential method. Fibonacci heaps. Disjoint sets. [Ref: CLRS3 chapters 17, 19, 20, 21] [about 4 lectures]
Geometric algorithms. Intersection of segments. Convex hull: Graham’s scan, Jarvis’s march. [Ref: CLRS3 chapter 33] [about 1 lecture]

At the end of the course students should:

have a thorough understanding of several classical algorithms and data structures;
be able to analyse the space and time efficiency of most algorithms;
have a good understanding of how a smart choice of data structures may be used to increase the efficiency of particular algorithms;
be able to design new algorithms or modify existing ones for new applications and reason about the efficiency of the result.

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Help shape the future of a revolutionary technology. Our online Master of Science in Engineering (MSE) in Artificial Intelligence (AI) degree program gives you the technical foundation you need to lead the development and implementation of AI systems — along with the broad perspective to deploy new tools in an ethical and responsible way.

Taught by some of the world’s leading experts in artificial intelligence, the program combines Ivy League excellence with the convenience of an entirely online curriculum. No matter where in the world you live, you’ll learn at your own pace and on your schedule.

The MSE-AI program explores the mathematical, computational and algorithmic foundation for AI and develops strong competencies in machine learning, statistical modeling and optimization. Students will be equipped with the latest knowledge on the data center infrastructures that are powering the AI revolution, namely distributed systems, networking and GPU programming. At the same time, gain understanding of the ethical implications of AI and prepare to mitigate its risks.

“AI is one of the most important areas in technology today. It’s generating an incredible amount of enthusiasm and a massive amount of investment. With this program, our goal is to educate a new generation of engineers who have the skills to analyze trends as they emerge — not only from the technological perspective, but also from the societal and ethical perspective. ”

Chris Callison-Burch

Professor, Computer and Information Science Department
Program Director, MSE-AI Online

“We’ve specifically designed our curriculum to provide both cutting-edge technical skills and an understanding of the impact this technology will have on people’s lives. Students will not only learn about the algorithms and models underpinning AI applications, they will also be equipped with the latest knowledge on the data center infrastructures that are powering the AI revolution, namely distributed systems, networking and GPU programming”

Boon Thau Loo

RCA Professor, Computer and Information Science Department
Associate Dean for Graduate Programs
Director, Distributed Systems Laboratory

MSE-AI Online Academic Journey

Mse-ai online course catalog.

Explore our extensive course catalog, providing a comprehensive overview of available courses, including detailed descriptions and prerequisites.

MSE-AI Online Sample Degree Plan

Students benefit from the support of a dedicated Student Success Advising Team, available to assist them in crafting tailored degree pathways and navigating various options for degree completion.

Expanding Your Academic Journey

Mse-ai online + online graduate certificate.

Gain expertise in crucial computer science areas and expand your career horizons with our certificates in Data Science and Software Systems.

Eligibility – Graduates of MSE-AI Online.

Academic Plan – If you graduated in the last five (5) years, you may transfer in two courses for your Certificate. Certificates consist of four courses from the certificate plan.

MSE-AI Online + Take a Course

Discover newly launched elective courses that delve into the latest advancements in technology. Embrace a shift in your learning focus to explore emerging areas, expanding your horizons to unveil a myriad of career possibilities.

Eligibility – Graduates of MSE-AI Online

Academic Plan – Join Penn Engineering Online for a single semester to boost your knowledge and discover new areas.

Try Before You Apply

Not sure if MSE-AI Online is for you? Try an open-enrollment specialization to find out if this interests you.

Artificial Intelligence and Machine Learning Essentials with Python Specialization

More From Forbes

Clemson to offer m.s. in computer science via coursera; no application required.

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Clemson University will offer an online M.S. in Computer Science for a total of $20,280 intuition.

Clemson University will partner with Coursera Coursera to offer a fully online Master of Science in Computer Science degree. The announcement was made in a blog release by Marni Baker Stein, chief content cfficer at Coursera , the online learning platform and a pioneer of Massive Open Online Courses (also known as MOOCs),

The program, which will have an artificial intelligence focus, is designed to be both affordable and uniquely accessible.

Instead of having to complete a formal application, students who hold a bachelor’s degree in any field from an accredited college and earn a B average in two introductory Clemson courses through Coursera will be automatically accepted. They will have 20% of the degree already completed.

Tuition for the complete program is set at $20,280 — 35% less than the comparable hybrid program.

“This Master of Science in Computer Science program is timely, industry-relevant and thoughtfully designed to be approachable to learners from many backgrounds, for example those looking for opportunities for mid-career advancement,” said Brian Dean, professor and C. Tycho Howle Director of the Clemson School of Computing, in the release.

“The modern and cutting-edge curriculum ensures that learners can succeed, whether they hold a formal computer science background or whether their computing background comes from prior real-world experience,” Dean added. “We are excited to be able to partner with Coursera to offer this program at Clemson University.”

Enrollment for the new program is scheduled to begin on May 1, 2024, with the first courses beginning in August 2024.

Clemson anticipates that most students will be able to complete the program in 20 to 36 months, preparing them for careers such as software development, information security analysis, and computer research. Students will be able to watch lecture videos at any time while engaging with peers and tenure-track Clemson faculty in live course sessions and office hours.

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The 10-course MSCS program will feature:

An AI-first curriculum. Five of the 10 courses will be focused on AI.
An emphasis on ethics. To promote ethical use of AI, students will be taught to examine the implications of each AI system before exploring it further.
A combination of theory with real-world skills. Students will first learn core software engineering principles before tackling more advanced topics, including deep learning, data science, and data mining.
A hands-on approach to learning. Students will be expected to complete complex projects in real-world computing environments, enabling them to build a substantial portfolio demonstrating they know how to apply their knowledge.

“We’re honored to partner with Clemson on this affordable, accessible, and incredibly relevant degree,” said Coursera’s Stein. “Together, we’ll educate future technical leaders, who will thoughtfully use AI to solve society’s most pressing challenges and create a positive impact.”

Clemson’s use of a performance-based admission process is an innovation that bears watching. While the use of standardized admissions tests continues to be hotly debated in higher education circles, actual course performance could prove a fairer and easier alternative for making admission decisions, particularly for certain graduate programs.

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Alum Alexander Levine Honored with Charles A. Caramello Distinguished Dissertation Award

University of Maryland Department of Computer Science alum Alexander Levine (Ph.D. '23, computer science) has been awarded the Charles A. Caramello Distinguished Dissertation Award for his dissertation titled "Scalable Methods for Robust Machine Learning." Levine, now a postdoctoral fellow at the University of Texas at Austin , focused on developing machine learning models that maintain accuracy amid distortions. The award ceremony is scheduled for Tuesday, May 14, at the Stamp Student Union. The award is for the dissertation he completed in 2023.

The Charles A. Caramello Distinguished Dissertation Award is given annually by the Graduate School to recognize dissertations that provide highly original contributions that make an unusually significant contribution to the discipline. Levine is among four recipients of the award this year.

Awardees receive an honorarium of $1,000. Additionally, they may be nominated for further recognition at the national level through the CGS/ProQuest Distinguished Dissertation Award competition, which selects outstanding dissertations from across the country to honor achievements in graduate research.

“I feel honored that my work has been recognized by this award,” Levine said. “I am deeply thankful for all of the support I received during my time at UMD from my advisor, my collaborators, my dissertation committee and the rest of the UMD computer science community. I am fortunate to have worked with such talented people on such interesting problems.”

Advised by Associate Professor Soheil Feizi , Levine's dissertation introduces innovative methods for ensuring the robustness of machine learning models, specifically in scenarios where input data may be subtly altered or distorted, including malicious tampering. This research is particularly relevant as machine learning applications become increasingly prevalent in areas requiring high reliability and security.

Levine explained that practitioners can implement these systems more confidently in safety-critical applications by developing machine learning techniques with well-understood robustness guarantees. He noted that the capabilities of machine-learning-based systems have expanded dramatically in just the last couple of years, increasing their use in various sectors. Levine emphasized the growing importance of ensuring these systems' robustness as their applications broaden.

Levine is currently expanding his research focus.

“At UT Austin, my research focus has shifted to representation learning for sequential decision-making tasks,” Levine shared. “In particular, I have been working on frameworks that allow deep learning to be used in combination with search-based planning techniques, so that we can benefit from both the powerful capabilities of modern deep learning and the interpretability, flexibility and efficiency of classical planning methods. ”

Levine received the Larry S. Davis Doctoral Dissertation Award in the Fall of 2023 . Named in honor of Computer Science Professor Emeritus Larry Davis , the award, given by UMD’s Department of Computer Science, highlighted dissertations that were exceptional in their technical depth and potential for significant impact.

—Story by Samuel Malede Zewdu, CS Communications

The Department welcomes comments, suggestions and corrections. Send email to editor [-at-] cs [dot] umd [dot] edu .

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MPhil in Advanced Computer Science

The Department of Computer Science and Technology will offer a part-time route to the PhD Degree with effect from October 2022.

Part-time structure

The Department of Computer Science and Technology could offer a part-time route to the PhD. At present, the University allows a part-time route which is 60% or 75% of a fulltime PhD route for which the minimum number of terms:

60% route -15 terms minimum; maximum number of terms for a part-time student is 21 terms.

75% route - 12 terms minimum; maximum number of terms for a part-time student is 16 terms.

The requirements for the probationary CPGS in Computer Science will be spread across two years with the first-year report due near the end of the fifth term (i.e. end of March for a Michaelmas admittee), and the registration viva occurring in the sixth term (Easter term). The Department expects the completion of the required 12 units from the Researcher Skills Programme across two years. Part-time students are also encouraged to spend one term full-time in the first year of the programme and that students will be in residence in Cambridge during that time.

After successful registration for the PhD Degree, part-time Ph.D. students are expected to have between 2 and 4 meetings with their supervisor per term for at least a further ten terms. They are expected to spend an average of three weeks each term in the Department with a minimum of 45 nights p.a. in residence.

Requirements for a part-time PhD applicants in Computer Science and Technology

The proposed topic needs to be suitable for study over a minimum of 12 or 15 terms (75% or 60% route respectively) and a maximum of 16 or 21 terms (75% or 60% route respectively) . Applicants will need to provide a schedule of the research over the first few years.
If a supervisor identifies a potential student and a topic as being possibly suitable for part-time study, an initial interview report form must be sent to the PhD Applications Panel for consideration.
Potential supervisors should invite the Chair of the PhD Applications Panel or a deputy to attend the formal interview.
As well as consideration by the PhD Applications Panel, the interview report will be considered by, and a decision approved by, the Degree Committee. The approved form will also be loaded to the applicant portal for consideration by the Postgraduate Admissions Office.
The proposed supervisor must be able to supervise a part-time Ph.D. for at least the minimum 15 terms. This means that supervisors on short-term contracts, or those due to retire within seven years of a part-time student being admitted, will not be eligible to supervise. Those who are due to take sabbatical leave should consider alternative supervision arrangements.
Applicants should be aware that there is no obligation on supervisors to accept applicants who wish to be admitted as part-time students.
The student must live close enough to Cambridge, or be able to spend enough time in Cambridge during the first two years, to be able to participate, as much as possible, in research group seminars, reading groups and other activities.
The student and supervisor will sign an agreement about how often the student will be in the department. This might be, for example : 2 x 8-hour days per working week per term, or 3 x 1-week per term, plus 40% of time in the research term (1 July to 30 September).
Most CST Research Skills courses are available remotely. For research themes’ group meetings and seminars, physical presence in the department is preferred.
The student will be required to provide a letter from the employer (if the student is employed) confirming that they may have time off to attend the University as required for the duration of the course. Applicants are required to upload a part-time attendance Declaration to their application once approved for admission.

Updated May 2024

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PhD in Computer Science
The PhD is the primary research degree that can be taken in the Department of Computer Science and Technology. The Cambridge PhD is a three to four-year full-time (five to seven-year part-time) programme of individual research on a topic agreed by the student and the Department, under the guidance of a staff member as the student's supervisor.
PhD in Computer Science
PhD in Computer Science . Department of Computer Science and Technology ... For those applicants who have not secured external scholarships and who wish to be considered for the various University and Cambridge Trusts' funding competitions, applications for the academic year commencing October 2024, and January 2025, open on 4 September 2023 ...
People: PhD students
List of PhD students PAT, recycling, and Building Services; Freshers. Freshers overview; Cambridge University Freshers' Events; Undergraduate teaching information and important dates; Course material 2022/23 Course material 2023/24 Exams. Exams overview; Examination dates; Examination results Examiners' reports Part III Assessment; MPhil Assessment
Department of Computer Science and Technology: List of PhD students
List of finished PhD students. Below is a list of all the PhD theses so far recommended by the Computer Science Degree Committee to the Board of Graduate Studies for approval (which can in some cases mean that there are still corrections to be made before final approval). Fully approved Cambridge PhDs are listed in the University Library thesis catalog.
Department of Computer Science and Technology
The Department of Computer Science and Technology (known as the Computer Laboratory) is an academic department within the University of Cambridge that encompasses Computer Science, along with many aspects of Technology, Engineering and Mathematics. The Department undertakes research in a broad range of subjects.
Department of Computer Science and Technology: List of current PhD students
student supervisor (co-supervisor) second adviser started expected finish subject Hugo Aaronson: Dr T. Gur: jv508: 2023-10-01: 2025-09-30: Quantum and classical sublinear algorithms
Department of Computer Science and Technology
PhD student's long journey to Cambridge. Research. Admissions. Current students. ... thanks to philanthropic support, are working on harnessing the power of computer science to tackle the twin crises of climate change and biodiversity loss. Read more at: ... University of Cambridge William Gates Building 15 JJ Thomson Avenue Cambridge
People: Faculty
List of PhD students PAT, recycling, and Building Services; Freshers. Freshers overview; Cambridge University Freshers' Events; Undergraduate teaching information and important dates; Course material 2022/23 Course material 2023/24 Exams. Exams overview; Examination dates; Examination results Examiners' reports Part III Assessment; MPhil Assessment
Department of Computer Science and Technology: Research
The PhD is the primary research degree offered in the Department of Computer Science and Technology. The Cambridge PhD is a three-year programme of individual research on a topic agreed by the student and the Department, under the guidance of a staff member as the student's supervisor. Students primarily work on their own project from the ...
PhD in Computer Science Program By University of Cambridge |Top
The PhD is the primary research degree that can be taken in the Department of Computer Science and Technology. The Cambridge PhD is a three to four-year full-time (five to seven-year part-time) programme of individual research on a topic agreed by the student and the Department, under the guidance of a staff member as the student's supervisor.
PhD in Scientific Computing
A common route for admission into our PhD programme is via the Centre's MPhil programme in Scientific Computing. The MPhil is offered by the University of Cambridge as a full-time course and introduces students to research skills and specialist knowledge. Covering topics of high-performance scientific computing and advanced numerical methods ...
MPhil and PhD programmes
Advanced Computer Science MPhil - The MPhil in Advanced Computer Science (the ACS) is designed to prepare students for doctoral research, whether at Cambridge or elsewhere. Typical applicants will have undertaken a first degree in computer science or an equivalent subject, and will be expected to be familiar with basic concepts and practices.
Department of Computer Science and Technology, University of Cambridge
The Department of Computer Science and Technology, formerly the Computer Laboratory, is the computer science department of the University of Cambridge. As of 2023 it employed 56 faculty members, 45 support staff, 105 research staff, and about 205 research students. [1] The current Head of Department is Professor Alastair Beresford.
MPhil in Advanced Computer Science
Continuing. The minimum requirement for continuation to the PhD programme in computer science is that MPhil students achieve an overall pass in the taught modules and, separately, the project. The pass mark is 60 per cent; however, higher minimum requirements may be set at the discretion of the Department and Degree Committee.
Computer Science
Prospective students apply through Harvard Griffin GSAS; in the online application, select "Engineering and Applied Sciences" as your program choice and select "PhD Computer Science" in the Area of Study menu. ... Cambridge, MA 02138-3654. Contact. Tel: 617-495-5315. Fax: 617-495-2928.
PhD Programme in Advanced Machine Learning
There are a number of funding sources at Cambridge University for PhD students, including for international students. All our students receive partial or full funding for the full three years of the PhD. ... The degree you would be applying for is a PhD in Engineering (not Computer Science or Statistics). How long does a PhD take? A typical PhD ...
PhD in Computer Science
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Distinguished Dissertations in Computer Science
The Conference of Professors of Computer Science (CPCS), in conjunction with the British Computer Society, selects annually for publication a few of the best British PhD dissertations in computer science. Its aim is to make more visible the significant British contribution to this field, and to provide a model for future students.
Department of Computer Science and Technology: List of PhD students by
Below is a list of finished and current PhD students admitted by the Computer Science Degree Committee, sorted by supervisor. Note: If a Computer Laboratory research student's PhD is not listed here, then it might have been dealt with by the Degree Committee for the Faculty of Mathematics (this includes any pre-1987 Computer Laboratory PhD) or by the Engineering Degree Committee (this applies ...
Computer Science, Ph.D.
About. The PhD in Computer Science offered at The University of Cambridge is the primary research degree that can be taken in the Computer Laboratory. The Cambridge PhD is a three-year programme of individual research on a topic agreed by the student and the Laboratory, under the guidance of a staff member as the student's supervisor.
Computer Science, BA (Hons) and MEng
Computer Science at Cambridge brings together disciplines including mathematics, engineering, the natural sciences, psychology and linguistics. Study modern computer science, along with the underlying theory and foundations in economics, law and business. Number 1 in the UK for Computer Science (The Complete University Guide 2024)
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The Department of Computer Science and Technology (formerly known as the Computer Laboratory) is the academic department within the University of Cambridge that encompasses computer science, along ...
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Department of Computer Science and Technology
The one-year MPhil in Advanced Computer Science is a general Masters degree, preparing students for PhD study in Computer Science. Doctoral courses. The PhD is the primary research degree that can be taken in the Computer Laboratory. The Cambridge PhD is a three-year programme of individual research on a topic agreed by the student and the ...
Department of Computer Science and Technology
Graph representations. Breadth-first and depth-first search. Topological sort. Minimum spanning tree. Kruskal and Prim algorithms. Single-source shortest paths: Bellman-Ford and Dijkstra algorithms. All-pairs shortest paths: matrix multiplication and Johnson's algorithms. Maximum flow: Ford-Fulkerson method, Max-Flow Min-Cut Theorem.
MSE-AI Academics
Designed specifically for students who are new to computer science, MCIT Online offers the same innovative curriculum and high-quality teaching as Penn's on-campus program. Build a strong foundation in computer science and gain real-world coding skills. Core courses and electives blend computer science theory and applied, project-based learning.
Clemson To Offer M.S. In Computer Science Via Coursera; No ...
Clemson University will offer an online M.S. in Computer Science for a total of $20,280 intuition. getty. Clemson University will partner with Coursera Coursera to offer a fully online Master of ...
Department of Computer Science and Technology
Research Proposal. Students are not assigned to pre-specified projects. They are expected to propose an area or topic, and will be accepted only if an appropriate and willing supervisor is available. Applicants should therefore prepare a statement of proposed research of no more than 3000 words (this is different from a personal statement ...
Alum Alexander Levine Honored with Charles A. Caramello Distinguished
University of Maryland Department of Computer Science alum Alexander Levine (Ph.D. '23, computer science) has been awarded the Charles A. Caramello Distinguished Dissertation Award for his dissertation titled "Scalable Methods for Robust Machine Learning." Levine, now a postdoctoral fellow at the University of Texas at Austin, focused on developing machine learning models that
Department of Computer Science and Technology
Part-time structure. The Department of Computer Science and Technology could offer a part-time route to the PhD. At present, the University allows a part-time route which is 60% of a fulltime PhD route for which the minimum number of terms for a part-time student is 15. The maximum number of terms for a part-time student is 21 terms.

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