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DeepMind PhD Studentship in AI or Machine Learning

About the Studentship

Queen Mary University of London is inviting applications for the DeepMind PhD Studentship for September 2023. 

The DeepMind PhD Studentship programme is established at Queen Mary University of London in partnership with leading British AI company, DeepMind. 

The PhD Studentship supports and encourages under-represented groups, namely female and Black researchers, to pursue postgraduate research in AI or Machine Learning. 

The PhD DeepMind Studentship will cover tuition fees and offer a London weighted stipend of £19,668 per year minimum together with an annual £2,200 travel and conference allowance and a one-off equipment grant of £1,700.

• 3-year fully-funded PhD Studentship
• Access to cutting-edge facilities and expertise in AI
• Partnership and mentorship with DeepMind employees working at the cutting edge of AI research and technologies.

Who can apply Queen Mary is on the lookout for the best and brightest students in the fields of AI and Machine Learning. 

Successful applicants will have the following profile:

• Identify as female and/or are of Black ethnicity, each being under-represented groups in the field of Artificial Intelligence and Computer Science
• Should hold, or is expected to obtain an MSc in Computer Science, Electronic Engineering, AI, Physics or Mathematics or a closely related discipline; or can demonstrate evidence of equivalent work experience
• Having obtained distinction or first-class level degree is highly desirable
• Programming skills are strongly desirable; however, we do not consider this to be an essential criterion if candidates have complementary strengths. 

We actively encourage applications from candidates who are ordinarily resident in the UK. The studentship is also open to International applicants. 

About the School of Electronic Engineering and Computer Science at Queen Mary

The PhD Studentship will be based in the School of Electronic Engineering and Computer Science (EECS) at Queen Mary University of London. As a multidisciplinary School, we are well known for our pioneering research and pride ourselves on our world-class projects. We are 8th in the UK for computer science research (REF 2021) and 7th in the UK for engineering research (REF 2021). The School is a dynamic community of approximately 350 PhD students and 80 research assistants working on research centred around a number of research groups in several areas, including Antennas and Electromagnetics, Computing and Data Science, Communication Systems, Computer Vision, Cognitive Science,  Digital Music, Games and AI, Multimedia and Vision, Networks, Risk and Information Management, Robotics and Theory

For further information about research in the school of Electronic Engineering and Computer Science, please visit: http://eecs.qmul.ac.uk/research/ .

How to apply

Queen Mary is interested in developing the next generation of outstanding researchers - whether in academia, industry or government – therefore the project undertaken under this Studentship is expected to fit into the wider research programme of School. Applicants should select a supervisor (a first and second choice) from the School at application stage. Visit our website for information about our research groups and supervisors:  eecs.qmul.ac.uk/phd/phd-opportunities/

Applicants should submit their interest by returning the following to  [email protected] by 12pm (noon), 10 April 2023:

• Indicate first and second choice academic supervisor 
• CV (max 2 pages) 
• Cover letter (max 4,500 characters)
• Research proposal (max 500 words) 
• 2 References 
• Certificate of English Language (for students whose first language is not English) 
• Other Certificates  

Application deadline: 10 April 2023

Applications will be reviewed by a panel of academic staff: May 2023

Interviews:  April/May 2023

Start date:  September 2023

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Postgraduate research project

Responsible and trustworthy ai, about the project.

The global artificial intelligence (AI) market is valued at over $387.4 billion and is projected to more than triple by 2029. For the world to realise the benefits brought by AI, it is important to ensure AI systems are responsibly developed, used throughout their entire life cycle, and trusted by the humans expected to rely on them.

This PhD studentship is in the broad area of responsible and trustworthy AI. Potential research topics include:

• multi-robot systems 
• autonomous systems
• multi-agent systems
• goal-driven systems
• recognition, human-computer interaction 

Modern AI technologies have great potential to advance our society. With this power comes great responsibility to ensure AI is used for good and in an equitable, transparent manner. AI has a significant impact, and ensuring that AI is designed, deployed, and used responsibly is critical to ensuring its impact is positive. 

The PhD studentship is related to our flagship £31M Responsible AI UK programme (RAI UK) . This national centre, based at the University of Southampton, will create an international ecosystem for responsible AI research and innovation. 

Potential supervisors

Lead supervisor.

Professor Gopal Ramchurn

Supervisors.

Dr Shoaib Ehsan

Research interests.

• Robotics (Localisation and Mapping)
• Embedded Systems
• Computer Vision

Entry requirements

A UK First class honours degree, or its international equivalent , in one of the following:

• computer science
• electronics
• mathematics
• a related discipline

The following are essential:

• attention to detail
• ability to work with diverse people and communities to understand their particular needs
• enthusiasm to work in the broader area of responsible and trustworthy AI to develop systems with beneficial human impact

This project is only open to UK applicants.

Fees and funding

For UK students, tuition fees will be paid and you'll receive a stipend (living allowance) of £18,622 tax-free per year for up to 3.5 years.

You need to:

• choose programme type (Research), 2023/24, Faculty of Engineering and Physical Sciences
• choose PhD in Computer science (Full time)
• add supervisor Professor Gopal Ramchurn in section 2

Applications should include:

• your CV (resumé)
• 2 reference letters
• degree transcripts to date
• a 2-page research proposal explaining what you're interested in, why it is exciting for you and what you have already read about it.

Your research idea could focus on the topics mentioned in the About the project section on the broader area of responsible and trustworthy AI. 

In your research proposal, we will be looking for your:

• excitement for your chosen topic
• enthusiasm for the opportunity to do research aligned with the Responsible AI UK programme and the UKRI Trustworthy Autonomous Systems (TAS) Hub
• ability to communicate your ideas clearly

Faculty of Engineering and Physical Sciences

If you have a general question, email our doctoral college ([email protected]) .

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Artificial Intelligence Machine Learning and Advanced Computing Postgraduate Research - 2024 Entry

Course details.

• Qualification PhD
• Duration 4 years

About This Course

One fully-funded 4-year PhD scholarship is available to start in September 2023 in the area of Artificial Intelligence machine learning and advanced computing (AIMLAC). The PhD is suitable for a graduate with a keen interest in AI algorithms for big data, optimisation, 3D modelling, and visualisation. The exciting project will research  Smart Optimisation of Big Data for Geometry Generation and 3D Models,  with Applications in Optimisation and Machine Learning of Big Data from various sources such as LiDAR (3D point clouds) obtained from architectural buildings and historical heritage.

The 4-year PhD, scholarship will sit within the  UKRI Centre for Doctoral Training in Artificial Intelligence, Machine Learning & Advanced Computing  (CDT-AIMLAC). The students will be based at Bangor University, located within the  School of Computer Science and Electronic Engineering  (CSEE). Funding will cover the full cost of tuition fees and an annual stipend of approximately £15,900.  Additional funding is available for research expenses.

Candidates must be resident in the UK without any immigration restriction. Applicants are required to submit a research proposal, on this topic, and written in their own words, when they submit their application. Candidates will be shortlisted, and then invited for interview.

Additional information of the project can be found  here .

Project title:  Smart Optimisation of Big Data for Geometry Generation and 3D Models

1st supervisor:  Dr Mosab Bazargani  (School of Computer Science and Engineering)

2nd supervisor:  Prof Jonathan C. Roberts (School of Computer Science and Engineering)

The successful candidate will be required to attend the AIMLAC taught components in year 1 (such as foundations of AI, research methods, information visualisation), residential meetings at Aberystwyth, Bristol, Cardiff or Swansea Universities, deliver responsible innovation, and engage with placements with external partners throughout the four-year programme. Placements may be six-month, or shorter three-month or two-week blocks. Successful applicants will be registered at Bangor University, hosted by the School of Computer Science and Engineering throughout their period of study, with the delivery of the related training in the PhD programme being shared between the Universities of Aberystwyth, Bangor, Bristol, Cardiff and Swansea. 

Entry Requirements

Applicants should have at least a 2:1 degree. Applicants must demonstrate excellent programming skills, and have followed a suitable degree programme, e.g., in computer science, mathematics or electronic engineering (with substantial programming), or closely related discipline. Applicants must have an interest in AI, machine learning and advanced computing and one of the topics, above. Applicants must have excellent written and spoken English. Applicants should have an aptitude and ability in computational thinking and methods (as evidenced by your degree and application information). Shortlisted candidates will be interviewed around the second half of July to the beginning of August. 

To qualify as a UK applicant, prospective students must have been ordinarily resident in the UK for three years immediately prior to the start of the award, with no restrictions on how long they can remain in the UK. Overseas applicants are not eligible, as we have met our quota that is applied across the whole AIMLAC CDT cohort. 

Application

To apply for the AIMLAC funded position at Bangor University, for the 2023 intake, applicants  must  complete Bangor’s PhD Direct Application process, and include the relevant and required information as below:

Select “Apply Now” from the menu. Applicants  must  include the following information.

• One research proposal , written in their own words, and based on the topic.
• An  up-to-date CV , evidencing suitable experience for the PhD positions.
• An  accompanying letter , including a statement of no longer than 1000 words that explains (a) why you want to join our Centre, and (b) your coding experience, with examples.
• certificates  and transcripts (if you are still an undergraduate, provide a transcript of results known to date),
• Academic  references  - all scholarship applications require two supporting references to be submitted. Please ensure that your chosen referees are aware of the funding deadline (to be determined), as their references form a vital part of the evaluation process. Please include these with your scholarship application.

Applicants must also complete equality, diversity and inclusivity information. This is a requirement of the funders. Due to collaborative nature of the award, this detail must also be submitted to the AIMLAC central email, separately to the application.  C omplete the  Monitoring Equality, Diversity and Inclusivity form  at time of your BU application.

Interviews (using video conferencing or in person) will occur during the second half of July to the beginning of August. 

The deadline for applications is August 17th 2023;  with interviews planned for the week starting 21 August, with a start date of 18th September.   However applications will be accepted until all positions are filled.

For more information please contact  Professor Jonathan Roberts

Explore More in Electronic Engineering

The advent of the digital era makes electronics and electronic devices more important than ever. Our world-leading experts expose our students to cutting-edge technologies and research. Our ambitions centre around employing micro and nanotechnology to exploit new materials and techniques.

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How we work in artificial intelligence

Ukri artificial intelligence centres for doctoral training.

The UK Research and Innovation (UKRI) artificial intelligence (AI) Centres for Doctoral Training (CDTs) are training a new generation of PhD students. They will develop novel AI methodology and use AI technology in areas such as:

• improving healthcare
• tackling climate change
• creating new commercial opportunities

2023 UKRI AI CDT funding opportunity

UKRI is investing a further £117 million to continue training doctoral researchers in AI across the remit of UKRI. The first cohort will start in the 2024 to 2025 academic year.

The outline proposal stage has now concluded. Successful outline proposals have been invited to submit full proposals by 13 July 2023. Find out about the UKRI AI CDT 2023 successful outline proposals .

Full details of the funding opportunity are available on the UKRI funding finder: UKRI Centres for Doctoral Training in artificial intelligence .

2018 UKRI AI Current UKRI AI CDTs

The £100 million investment in 2018 supported CDTs based at 14 universities and include partnerships with over 300 organisations including:

• AstraZeneca
• Rolls-Royce

This investment leveraged an additional £78 million from project partners and £23 million from the universities. Around 1000 students will be trained over 8 years.

Contact details

Email: [email protected]

The centres are:

UKRI AI Centre for Doctoral Training in Foundational Artificial Intelligence Led by Professor David Barber, University College London

UKRI AI Centre for Doctoral Training in AI Enabled Healthcare Systems Led by Professor Geraint Rees, University College London

UKRI AI Centre for Doctoral Training in Environmental Intelligence: Data Science and AI for Sustainable Futures Led by Professor Gavin Shaddick, University of Exeter

UKRI AI Centre for Doctoral Training in Natural Language Processing Led by Professor Mirella Lapata, The University of Edinburgh

UKRI AI Centre for Doctoral Training in Artificial Intelligence and Music Led by Professor Simon Dixon, Queen Mary University of London

UKRI AI Centre for Doctoral Training in Speech and Language Technologies and their Applications Led by Professor Thomas Hain, The University of Sheffield

UKRI AI Centre for Doctoral Training in AI for Healthcare Led by Dr Aldo Faisal, Imperial College London

UKRI AI Centre for Doctoral Training in Accountable, Responsible and Transparent AI Led by Professor Eamonn O’Neill, University of Bath

UKRI AI Centre for Doctoral Training in Artificial Intelligence, Machine Learning & Advanced Computing Led by Professor Gert Aarts, Swansea University

UKRI AI Centre for Doctoral Training in Machine Intelligence for Nano- Electronic Devices and Systems Led by Professor Tim Norman, University of Southampton

UKRI AI Centre for Doctoral Training in Socially Intelligent Artificial Agents Led by Professor Alessandro Vinciarelli, University of Glasgow

UKRI AI Centre for Doctoral Training in Biomedical Artificial Intelligence Led by Professor Ian Simpson, The University of Edinburgh

UKRI AI Centre for Doctoral Training in Interactive Artificial Intelligence Led by Professor Peter Flach, University of Bristol

UKRI AI Centre for Doctoral Training in AI for the Study of Environmental Risks Led by Dr Emily Shuckburgh, University of Cambridge

UKRI AI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence Led by Dr Elizabeth Black, King’s College London

UKRI AI Centre for Doctoral Training in Artificial Intelligence for Medical Diagnosis and Care Led by Professor David Hogg, University of Leeds

Meeting reports

UKRI AI CDT meeting reports

Last updated: 8 April 2024

This is the website for UKRI: our seven research councils, Research England and Innovate UK. Let us know if you have feedback or would like to help improve our online products and services .

Artificial intelligence and machine learning

Our research group investigates artificial intelligence and machine learning, including the nature of intelligence and how to build intelligent systems..

Find out more about us and join us at one of our seminars.

Artificial intelligence and machine learning group members

We are a team of academics exploring the nature of intelligence and building intelligent systems.

Artificial intelligence and machine learning seminars

We have an active seminar series with local, national and occasionally international speakers. There are also many relevant seminars and collaborations.

Find out how to join us as a PhD student.

Postgraduate research degrees in computer science

Find out about our PhD degrees, funding opportunities and how you can apply.

UKRI CDT in Accountable, Responsible and Transparent AI

We train the next generation of specialists with expertise in AI, its applications and its implications.

Machine learning

• Learning on data manifolds: semi-supervised learning, spectral clustering, non-linear data embedding, link prediction
• Bayesian inference: Large-scale approximate Bayesian inference, latent variable models
• Learning for computer graphics: Bayesian inference for shape modelling, tracking, sampling, and transfer, machine learning for computational photography, videography, and 3D data analysis
• Sparse Bayesian models (the “relevance vector machine”) and related novel learning techniques
• Probabilistic approaches to tree-based pattern recognition
• Adaptive analysis of multivariate time series
• Methods for intelligent statistical automation
• New perspectives on deep neural networks
• Model-driven data mapping and visualisation techniques
• Reinforcement learning

Autonomous systems and Agents

Autonomous systems.

• Synthetic emotions, and their impact on Human Robot Interaction
• Intelligent perception, planning and control for autonomous driving
• Coordination and mapping with UAVs: path planning driven by map and 3D image construction
• Robot and AI ethics: the ethical design of intelligent systems and their role in society
• Intelligent assistance for level design, and procedural generation of game content more generally
• Agent-based modelling/simulation
• Agent architecture
• Agents on the web
• Semantic web technologies
• Software and systems engineering for multi-agent systems and AI

Natural language processing and Knowledge representation and reasoning

Natural language processing.

• Development of computational models for understanding and generating human language
• AI and machine learning applications to language tasks
• Text classification and text mining
• Applications of NLP: educational technology, intelligent tutoring

Knowledge representation and reasoning

• Answer Set Programming (ASP) and its applications
• Norms and institutions as mechanisms for analysis and control
• Sensor networks
• Applications (e.g. music composition, planning, legal reasoning)

Research outputs

Take a look at recent papers, articles and conference contributions from our staff and students on Bath research portal.

Computer Science

PhD in Safe AI (SAINTS CDT)

Join our thriving multidisciplinary community and deliver projects focused on the safety of AI.

Join our thriving multidisciplinary community and deliver projects focused on the safety of AI. Funded by UKRI, the SAINTS CDT brings together people from diverse disciplines such as computer science, philosophy, law, sociology and economics to create the next generation of safe AI experts and a lasting community that will pioneer new evidence-based policy and practices for safe AI.

Doctoral training and support

A multidisciplinary approach.

Our CDT in Safe AI takes a unique and purposeful approach. Postgraduate researchers with different backgrounds and experiences will train together, focusing on ‘grand challenges’ around one of SAINTS' core research themes:

• Life-long safety of AI: Safety-driven design and training for evolving contexts; testing for open and uncertain operating environments; safe retraining and continual learning; proactive monitoring procedures and dynamic safety cases; ongoing assurance of societal and ethical acceptability.
• Safety of increasingly autonomous AI: Understanding human-AI interaction to design safe joint cognitive systems; the assurance of safe transition between human and AI control; achieving effective human oversight and AI explainability; preserving human autonomy and responsibility.

By undertaking relevant and societally important research, you will become part of a world-class community of professionals who will pioneer a new generation of evidence-based methodologies and practices for safe AI-enabled autonomous systems.

You will also participate in a series of taught courses covering the technical, legal, ethical and societal underpinnings and implications of your research. These courses will give you the strong foundation you need for both your research and the next step in your career.

[email protected]

Related links

• Reasons to choose York
• Life in York
• Information for international students
• SAINTS FAQs

Under the guidance of your supervisor, you'll work towards a final thesis of 80,000 words for the PhD. A typical semester will involve a great deal of independent research, with guidance from your supervisor who will be able to suggest direction and address concerns. You'll work independently in a research environment that thrives on creativity and scholarship.

Successful applicants will be among the first scholars in the world to take the next step into an AI-powered future, and to shape and contribute to the innovation of AI and autonomous systems. Professor Ibrahim Habli, Director of SAINTS CDT

Work directly with industry

The SAINTS CDT has an extensive network of over thirty partners, including major international companies, government organisations, regulators and charities. Our industry partners will be closely involved in the research that you'll undertake throughout your PhD. They are also committed to providing other forms of support, from real-world data and case studies to guest lectures and access to facilities.

Our partners will provide you with 'research exchange' opportunities where, as part of your PhD, you will work in industry to gain valuable experience that will support your research development. Some of our partners also expect to offer employment opportunities to SAINTS researchers when they graduate.

for Computer Science and Information Systems in the QS World University Rankings by Subject, 2023.

Interdisciplinary expertise

Funded by UKRI, the SAINTS CDT brings together people from diverse disciplines such as computer science, philosophy, law, sociology and economics to create the next generation of experts pioneering new evidence-based policy and practices for safe AI.

Research excellence

We are in the UK top ten for research in computer science, philosophy and health science - according to the Times Higher Education's ranking of the latest REF results (2021).

Financial benefits

As a postgraduate researcher with SAINTS CDT, you'll receive the following financial benefits:

• A tax-free stipend of £18,622 per year for your living costs (pro rata for part-time postgraduate researchers), which is paid to you in regular instalments. The amount of the stipend usually increases each year in line with inflation
• Your annual tuition fees will also be paid and there will be funding available for you to attend relevant events and conferences

Your career

Your PhD in Safe AI will train you to work at the cutting edge of AI development and safety assurance. You'll gain the ability to engage deeply with emerging regulatory, ethical and policy-making aspects of AI governance.

Throughout your degree, your supervisor will monitor your progress, and will help you to hone the focus of your research.

Having worked extensively with our partners, you'll be able to demonstrate the ability to translate your research and align it with industrial, regulatory and societal needs. You'll have the skills to move from doctoral research to AI-AS safety roles in industry, regulation and the public sector, as well as to postdoctoral fellowships.

Careers and skills

SAINTS has appointed a member of the academic team as Careers Lead, and they will support you as you create a professional development plan to progress your chosen career path. To assist you further, you'll also have input from your supervisors and mentors from partner organisations wherever possible.

Our multidisciplinary approach to doctoral research means that you will be well-placed to lead the growth of responsibly developed and trustworthy AI-enabled autonomous systems in the future.

Our dedicated careers team offer specific support, including a programme of professional researcher development and careers workshops and 1:1 career support sessions . They will help you with your employability portfolio, and to engage in activities that will build up your skills and experience within and outside of your research work.

Course location

You'll be based at the world's first Institute for Safe Autonomy at the University of York.

It's an exciting and welcoming hub for innovation and collaboration with a modern and inclusive working environment. You'll benefit from world-class laboratories, collaboration spaces and expert colleagues working at the leading edge of their fields.

Most of your training and supervision meetings will take place on campus at the University of York, though your research may take you further afield.

Entry requirements

Typically, you should have, or expect to obtain, one of the following combinations of academic qualification:

• A first-class honours degree (or equivalent), OR
• 2:1 in an honours degree and a Master’s degree (or equivalent)

We recognise that applicants may not have followed a traditional career path, and therefore relevant work experience or equivalent qualifications can be taken into account. 

Your knowledge and experience should enable you to undertake doctoral research in your chosen area within the SAINTS CDT (eg computer science, engineering, mathematics, economics, health sciences, law, philosophy, sociology).

While you may not have a degree in computer science, we expect that you will have basic programming skills. For successful applicants that do not have these skills at the required level, we will hold a programming 'bootcamp' before the start of the academic year.

Prior practical experience in creating AI-enabled systems is desirable, but is not essential.

English language requirements

If English is not your first language you must provide evidence of your ability.

Check your English language requirements

At the start of your applicant journey, you’ll complete an application form detailing your academic qualifications and any relevant work experience you may hold. You’ll also be asked to provide information about your key skills that are relevant to AI and team working. You don’t need to identify a potential supervisor or submit a research proposal at this stage, and it is not necessary to submit your certificates or transcripts.

All stage one applications received will be anonymised and carefully assessed by our academic team, making sure that all applicants are treated equitably. Successful applicants will be invited to join stage two of the recruitment process.

If you successfully go through to stage two, you will be invited to submit a formal Expression of Interest to demonstrate how your research interests align with one of the research themes within the CDT. Our academic team will assess your expression of interest and will look for evidence of your potential to undertake research in your chosen area.

Applicants successfully completing this stage will be invited to the third and final stage of the recruitment process.

Stage three

Applicants reaching stage three will be invited to a full-day group selection event at the University of York. During round-table discussions, you’ll be assessed on your approach to collaboration. You’ll also attend an interview with a panel consisting of members of the academic team.

If you are unable to attend the selection event in York, you will be invited to a group selection event held online.

Applications will open for 2025 entry in Autumn 2024.

If you have any further questions, we'd recommend visiting our FAQ page . Alternatively, contact us .

Discover York

Accommodation

We offer a range of campus accommodation to suit you and your budget, from economy to deluxe.

Discover more about our researchers, facilities and why York is the perfect choice for your research degree.

Graduate Research School

Connect with researchers across all disciplines to get the most out of your research project.

• 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.

UKRI CDT In Foundational AI

Programme structure

Partnerships, publications.

Foundational Artificial Intelligence MPhil/PhD

Applications for the cdt are now closed.

The UKRI CDT in Foundational AI is no longer accepting new applications and has completed it's cohort intakes for the years 2019 - 2023. The CDT is funded via the UKRI for a period of 8 years, 2019-2027, and will therefore no longer be taking on new students after September 2023. 

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About the course

Our Foundational AI CDT addresses the national need for AI workers by training researchers capable of advancing core AI algorithms. These graduates will help shape the social, scientific and economic landscape through scientific breakthroughs and the creation of companies on the basis of novel AI technology.

Current AI machines are largely "dumb" — they don't understand their physical environment, nor have enough understanding of human culture to communicate in natural ways. Our vision is that AI is in its infancy and that AI breakthroughs are key to controlling and shaping the future technological landscape. However, creating effective AI is challenging given our limited understanding of how intelligence works.

In forging AI creators, we will therefore encourage students to look beyond Computer Science and be open to interaction with other scientists researching intelligence.

The most successful existing AI technology (Deep Learning) is based on the way brains process information. Future developments may be inspired by neuroscience and we need to be alert to the insights offered by this and other fields.

A key research objective of CDT students is to make new algorithms for next-generation AI technologies by incorporating more knowledge about the real-world and human culture into the AI agents themselves. Such machines will be able to offer unparalleled insights into our data-rich world and provide us with transparent and interpretable explanations.

A unique aspect of the CDT is to give students the deep technical skills they require to be leading researchers in AI and also the skills to be a deep tech entrepreneur.

The CDT includes a programme of cohort-based training activities designed to encourage interaction with other students on the CDT, through shared research, training and social experiences, including in AI and entrepreneurship. These training elements are additional to a standard PhD programme and students need in their personal statements to recognise the alignment of this CDT approach with their interests and aspirations. Applicants that do not explain why the CDT mechanism is particularly appropriate for them will be at a significant disadvantage.

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Course type

Qualification, university name, part time phd artificial intelligence (ai).

11 degrees at 9 universities in the UK.

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PhD Robotics

Sheffield hallam university.

Course summary Undertake extensive, supervised studies in the Centre for Automation and Robotics Research Specialise in pertinent Read more...

• 4 years Full time degree: £4,712 per year (UK)
• 7 years Part time degree: £2,356 per year (UK)

Robotics and Autonomous Systems PhD

University of surrey.

Why choose this programme On our Robotics and Autonomous Systems PhD, you’ll study, design and build novel solutions and behaviours for Read more...

• 8 years Part time degree: £2,356 per year (UK)

Computer Science PhD, MPhil - Knowledge Discovery and Machine Learning

University of leicester.

Computing at Leicester offers supervision for the degrees of Doctor of Philosophy (PhD) - full-time and part-time Master of Philosophy Read more...

• 3 years Full time degree: £4,786 per year (UK)
• 6 years Part time degree: £2,393 per year (UK)

PhD Robotics and Systems Engineering

University of salford.

INTRODUCTION Automation for the Food Industry Research The food industry is very labour intensive and as a result is under threat from Read more...

• 3 years Full time degree: £4,780 per year (UK)
• 5 years Part time degree: £2,390 per year (UK)

Artificial Intelligence Enabled Healthcare MRes and MPhil/PhD

Ucl (university college london).

The CDT programme consists of a 1 year MRes followed by a 3 year PhD. Throughout this period the CDT will continue to closely monitor the Read more...

• 1 year Full time degree: £6,035 per year (UK)
• 2 years Part time degree: £2,930 per year (UK)

DPhil in Autonomous Intelligent Machines and Systems (EPSRC Centre for Doctoral Training)

University of oxford.

The Autonomous Intelligent Machines and Systems (AIMS) Centre for Doctoral Training (CDT) provides graduates with the opportunity to Read more...

• 4 years Full time degree: £9,500 per year (UK)
• 8 years Part time degree: £4,750 per year (UK)

Text and Data Mining (PhD/MPhil)

Cardiff university.

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Informatics: ANC: Machine Learning, Computational Neuroscience, Computational Biology PhD

The university of edinburgh.

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PhD Intelligent Systems

Ulster university.

The vision is to develop a bio-inspired computational basis for Artificial Intelligence to power future cognitive technologies. Our mission Read more...

• 3 years Full time degree: £4,712 per year (UK)
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Statistics and Machine Learning (DPhil)

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International Edition

• Artificial Intelligence /

UK mulling potential AI regulation

The country’s ai safety institute has been evaluating ai models for safety even without an official regulatory framework in place..

By Emilia David , a reporter who covers AI. Prior to joining The Verge, she covered the intersection between technology, finance, and the economy.

Share this story

Officials at the UK’s Department of Science, Innovation and Technology have started drafting legislation to regulate AI models, Bloomberg reports . It’s unclear how any future regulation will intersect with the UK’s already-extant AI Safety Institute, which already conducts safety tests of the most powerful AI models.

After hosting the first global AI Safety Summit at Bletchley Park in November 2023, which was attended by many world leaders , the UK established an AI Safety Institute the following November. The institute began evaluating AI models for safety this year, though some technology companies requested more clarity on the timelines and what would happen if AI models are found risky. The UK also agreed to do joint safety testing of models with the US. 

However, the UK does not officially have a policy preventing companies from releasing AI models that have not been evaluated for safety. Neither does it have the power to pull any existing model from the market if it violates safety standards or to fine a company over those violations. (In comparison, the European Union’s AI Act can impose fines if AI companies violate certain safety benchmarks.)

Prime Minister Rishi Sunak has previously said there’s no need to “ rush to regulate ” AI models and platforms. Meanwhile, Bloomberg reports, other government officials have raised the possibility of amending the UK’s copyright rules to strengthen the opt-out option for training datasets. Any potential bill is still a ways off, according to Bloomberg.

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