phd in machine learning in uk

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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.

• Schools & departments

Centre for Doctoral Training in Machine Learning Systems PhD with Integrated Study

Awards: PhD with Integrated Study

Study modes: Full-time

Placements/internships

Discovery Day

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Research profile

Machine Learning ( ML ) has a great impact on our daily lives. Developments in ML are built on improved systems that can train and generate increasingly powerful models. Systems design greatly impacts ML performance and capability.

Major advancements are made when ML and systems are developed and optimised together. This is relevant across many industries such as:

in-car systems

medical devices

mobile phones

sensor networks

condition monitoring systems

high-performance computing

the creative industries

patient care

social networking

high-frequency trading

However, PhD training that combines systems and ML is rare as research training is often separated into individual sub-disciplines.

Instead, we need researchers trained in both fields and experienced in working across them. This ML Systems PhD involves training collaborative researchers with experience across systems and ML .

The programme is about machine learning that works to deliver for a need. It involves a holistic view of machine learning and systems that includes both a user-centric approach and an understanding of how to make things work.

Programme structure

The programme is a 4-year PhD with integrated study where you will take 180 credits of courses over years 1-3, while carrying out your PhD project research. As part of your studies, you will do an internship either in a company or the public sector (usually for 3-6 months) or an alternative form of engagement.

In the first year, you will take courses on Machine Learning Systems, Machine Learning Practical and Controversies in a Data Society. There will also be an introductory research project which will form the basis of your PhD project.

The programme is flexible to accommodate students from varying backgrounds, and the final programme of study will be agreed between the student, supervisors, and Doctoral Programme organisers.

The learning objectives for this PhD programme are:

world-leading research in an area of ML Systems and distributing that research through methods such as publication

develop expertise in an area of ML -Systems with an understanding of the full ML -Systems stack

experience of interacting with researchers from other areas of expertise

knowledge of different research environments in academia, companies and the public sector

deep understanding of the ethical, societal and international issues on the use and deployment of ML methods

skills in communicating to technical and non-technical audiences

active involvement in knowledge transfer and public engagement

organisation and leadership skills and experience

Work placements/internships

You will usually do an internship as part of the programme, but alternatives to company internships can be arranged if you prefer.

Training and support

You will be supported in your study by:

two supervisors

a team of researchers associated with the research group

peer interaction and learning opportunities

training delivered by Edinburgh staff and invited lecturers

opportunities for entrepreneurship training

outreach and public communication training

dedicated administrative staff for the programme

You will be part of the vibrant world-class and interdisciplinary research community in the Informatics Forum and Bayes Centre. This will give you access to state-of-the-art computational infrastructure through the School of Informatics and EPCC including large GPU cluster computing in the EIDF .

Career opportunities

Business analysts predict AI -enhanced consumer products will be the highest contributor to UK economic gains in the next decade. Therefore, there is a growing demand for PhD graduates in this area to lead this innovation. This is evidenced by the rapid growth in starting salaries and the increasing distinction between Data Scientists and ML Systems Engineers.

Entry requirements

These entry requirements are for the 2024/25 academic year and requirements for future academic years may differ. Entry requirements for the 2025/26 academic year will be published on 1 Oct 2024.

A UK 2:1 honours degree, or its international equivalent, in an area relevant to the CDT, for example, informatics, computer science, AI, cognitive science, mathematics, physics, engineering, or in another field with sufficient additional evidence of capability in the required areas.

International qualifications

Check whether your international qualifications meet our general entry requirements:

• Entry requirements by country
• English language requirements

Regardless of your nationality or country of residence, you must demonstrate a level of English language competency at a level that will enable you to succeed in your studies.

English language tests

We accept the following English language qualifications at the grades specified:

• IELTS Academic: total 6.5 with at least 6.0 in each component. We do not accept IELTS One Skill Retake to meet our English language requirements.
• TOEFL-iBT (including Home Edition): total 92 with at least 20 in each component. We do not accept TOEFL MyBest Score to meet our English language requirements.
• C1 Advanced ( CAE ) / C2 Proficiency ( CPE ): total 176 with at least 169 in each component.
• Trinity ISE : ISE II with distinctions in all four components.
• PTE Academic: total 62 with at least 59 in each component.

Your English language qualification must be no more than three and a half years old from the start date of the programme you are applying to study, unless you are using IELTS , TOEFL, Trinity ISE or PTE , in which case it must be no more than two years old.

Degrees taught and assessed in English

We also accept an undergraduate or postgraduate degree that has been taught and assessed in English in a majority English speaking country, as defined by UK Visas and Immigration:

• UKVI list of majority English speaking countries

We also accept a degree that has been taught and assessed in English from a university on our list of approved universities in non-majority English speaking countries (non-MESC).

• Approved universities in non-MESC

If you are not a national of a majority English speaking country, then your degree must be no more than five years old* at the beginning of your programme of study. (*Revised 05 March 2024 to extend degree validity to five years.)

Find out more about our language requirements:

• Academic Technology Approval Scheme

If you are not an EU , EEA or Swiss national, you may need an Academic Technology Approval Scheme clearance certificate in order to study this programme.

Fees and costs

Scholarships and funding.

Search for scholarships and funding opportunities:

• Search for funding

Further information

• CDT Manager
• Phone: +44 (0)131 650 9989
• Contact: [email protected]
• CDT Director, Prof Amos Storkey
• Phone: +44 (0)131 651 1208
• Contact: [email protected]
• Doctoral Training in Machine Learning Systems
• School of Informatics
• 10 Crichton Street
• Central Campus
• School: Informatics
• College: Science & Engineering

Select your programme and preferred start date to begin your application.

PhD with Integrated Study in Machine Learning Systems - 4 Years (Full-time)

Application deadlines.

Applicants requiring an ATAS certificate should apply by 7 April. All other applicants should apply by 26 April.

• How to apply

You must submit two references with your application.

You must submit an application via the EUCLID application portal and provide the required information and documentation.

This will include submission of:

• a Curriculum Vitae (CV)
• a research proposal (2-3 pages long)
• degree certificates and official transcripts of all completed and in-progress degrees (plus certified translations if academic documents are not issued in English).
• two academic references

Only complete applications will progress forward to the academic selection stage.

Read through detailed guidance on how to apply for a PGR programme in the School of Informatics:

• School of Informatics PGR Application Guidance

Find out more about the general application process for postgraduate programmes:

Imperial College London Imperial College London

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• Data Science Institute

The DSI hosts a number of PhD students, funded from a variety of mechanisms including industry, research funders and self-funded. All applications for a PhD programme need to be submitted through the department where the chosen supervisor sits. For example, if the supervisor is hosted in the Department of Computing, visit  this page with relevant information about the application process.

The DSI are currently advertising for a PhD studentship in collaboration with the China State Shipbuilding Corporation (CSSC) and Jiangsu Automation Research Institute (JARI) to produce the next generation of Data Scientists, if you are interested you can find further information on our vacancy page . The closing date for applicants is 28th February 2021. 

Imperial College London received funding from UKRI for a Centre for Doctoral Training in  AI for Healthcare  which is currently open for applications. More information on the CDT can be found  here .

Axel Oehmichen

"This dual position as a researcher and a student has proven extremely rich in experiences as I was learning and collaborating with other DSI researchers across different fields."

Dr Axel Oehmichen

Axel on his time at the DSI; "I was a part-time PhD student and a research associate working on the eTRIKS and OPAL projects. My research focused on the development of a new platform called the eTRIKS Analytical Environment (eAE) as an answer to the needs of analysing and exploring massive amounts of medical data in a privacy preserving fashion. This dual position as a researcher and a student has proven an extremely enriching experiences as I was learning and collaborating with other DSI researchers across different fields. Those collaborations have brought me new perspectives, allowed me to explore new fields and helped me grow as a researcher. I am an engineer by training and, while it was sometimes challenging, that duality made it possible to join both worlds during my PhD and facilitated my transition to the start-up world". 

Hao Dong  

Akara Supratak Akara Supratak was a PhD student at the Data Science Institute (DSI) from 2013 to 2017, supervised by Professor Yike Guo. During his PhD, he has developed a deep learning model, named DeepSleepNet, for automatic sleep stage scoring, which can achieve state-of-the-art performance ( https://github.com/akaraspt/deepsleepnet ). The study at DSI has given him an opportunity to learn and work with other researchers across different fields such as distributed computing and health informatics, and has broadened his knowledge and experience in doing frontier research.

What is he doing now : He is an instructor at the Faculty of Information and Communication Technology (ICT), Mahidol University, Thailand. Currently, he teaches several courses for undergraduate students such as Fundamentals of Programming and Computer Architecture. His research focuses on Machine Learning, Biosignal Processing, and Image Processing.

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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 

Statistics and Machine Learning (EPSRC CDT)

• Entry requirements
• Funding and costs

College preference

• How to Apply

About the course

The Statistics and Machine Learning (StatML) Centre for Doctoral Training (CDT) is a four-year DPhil research programme (or eight years if studying part-time). It will train the next generation of researchers in statistics and machine learning, who will develop widely-applicable novel methodology and theory and create application-specific methods, leading to breakthroughs in real-world problems in government, medicine, industry and science. 

This is the Oxford component of StatML, a CDT in Statistics and Machine Learning, co-hosted by Imperial College London and the University of Oxford. The programme will provide you with training in both cutting-edge research methodologies and the development of business and transferable skills – essential elements required by employers in industry and business.

You will undertake a significant, challenging and original research project, leading to the award of a DPhil. Given the breadth and depth of the research teams at Imperial College and at the University of Oxford, the proposed projects will range from theoretical to computational and applied aspects of statistics and machine learning, with a large number of projects involving strong methodological/theoretical developments together with a challenging real problem. A significant number of projects will be co-supervised with industry.

You will pursue two mini-projects during your first year (specific timings may vary for part-time students), with the expectation that one of them will lead to your main research project. At the admissions stage you will choose a mini-project. These mini-projects are proposed by the department's supervisory pool and industrial partners. You will be based at the home institution of your main supervisor of the first mini-project.

If your studentship is funded or co-funded by an external partner, the second mini-project will be with the same external partner but will explore a different question.

Alongside your research projects you will engage with taught courses each lasting for two weeks. Core topics will be taught during at the beginning of your first year (specific timings may vary for part-time students) and are:

• Modern Statistical Theory
• Statistical Machine Learning;
• Causality; and
• Bayesian methods and computation.

You will then begin your main DPhil project at the beginning of the third term (at the beginning of the fourth term for part-time students), which can be based on one of the two mini-projects. Where appropriate for the research, your project will be run jointly with the CDT's leading industrial partners, and you will have the chance to undertake a placement in data-intensive statistics with some of the strongest statistics groups in the USA, Europe and Asia.

If you are studying full-time, starting in the second year, you will teach approximately twelve contact hours per year in undergraduate and graduate courses in your host department. If you are studying part-time, teaching will begin in the third year and you will teach approximately six hours per year. This is mentored teaching, beginning with simple marking, to reach a point where individual students are leading whole classes of ten or twelve undergraduate students. Students will have the support of a mentor and get written feedback at the end of each block of teaching.

You will also be required to take a number of optional courses throughout the four years of the course, which could be made up of choices from the following list: Bayesian nonparametrics; high-dimensional statistics; advanced optimisation; networks; reinforcement learning; large language models; conformal inference, variational Bayes and advance Bayesian computations, dynamical and graphical modelling of multivariate time series, modelling events; and deep learning. Optional modules last two weeks and are delivered in a similar format to the core modules.

Many events bring StatML students and staff together across different peer groups and research groups, ranging from full cohort days and group research skills sessions to summer schools. These events support research and involve staff and students from both Oxford and Imperial coming together at both locations.

The Department of Statistics runs a seminar series in statistics and probability, and a graduate lecture series involving snapshots of the research interests of the department. Several journal-clubs run each term, reading and discussing new research papers as they emerge. These events bring research students together with academic and other research staff in the department to hear about on-going research, and provide an opportunity for networking and socialising.

Further information about part-time study

As a part-time student you will be required to attend modules and other cohort activities in Oxford (or sometimes London) for a minimum of 30 days each year. There will be no flexibility in the dates of modules or cohort events, though it is possible to spread your attendance at modules over the course of the four year programme (with agreement of your supervisor and the programme Directors). Attendance will be required during term-time (on a pro-rata basis) for cohort activities. These often take place on Mondays and Thursdays. Attendance will occasionally be required outside of term-time for cohort activities. 

You will have the opportunity to tailor your part-time study and skills training in liaison with your supervisor and programme Directors, and agree your pattern of attendance.

Supervision

The allocation of graduate supervision for this course is the responsibility of the Department of Statistics (Oxford) and/or the Department of Mathematics (Imperial). It is not always possible to accommodate the preferences of incoming graduate students to work with a particular member of staff. A supervisor may be found outside these departments.

You are matched to your supervisor for the first mini-project at the start of the course. Within the first year of the course, the student will have the opportunity to work with an alternative supervisor for a second mini-project. It is normal for one of these mini-projects to lead to the full DPhil project with the same supervisory team as was in place for the mini-project chosen. 

Typically, as a research student, you should expect to have meetings with your supervisor or a member of the supervisory team with a frequency of at least once every two weeks averaged across the year. The regularity of these meetings may be subject to variations according to the time of the year, and the stage that you are at in your research programme.

Each mini-project will be assessed on the basis of a report written by the student, by researchers from Imperial and Oxford.

Modules are assessed by a presentation in small groups on some material studied during the two-week module (known as micro-projects within the programme).

All students will be initially admitted to the status of Probationer Research Student (PRS). Within a maximum of six terms as a full-time PRS student or twelve terms as a part-time PRS student, you will be expected to apply for transfer of status from Probationer Research Student to DPhil status. This application is normally made by the fourth term for full-time students and by the eighth term for part-time students.

A successful transfer of status from PRS to DPhil status will require the submission of a thesis outline. Students who are successful at transfer will also be expected to apply for and gain confirmation of DPhil status to show that your work continues to be on track. This will need to done within nine terms of admission for full-time students and eighteen terms of admission for part-time students.

Both milestones normally involve an interview with two assessors (other than your supervisor) and therefore provide important experience for the final oral examination.

Full-time students will be expected to submit a thesis at four years from the date of admission. If you are studying part-time, you be required to submit your thesis after six or, at most, eight years from the date of admission. To be successfully awarded a DPhil in Statistics you will need to defend your thesis orally (viva voce) in front of two appointed examiners.

The final thesis is normally submitted for examination during the fourth year (or eighth year if studying part-time) and is followed by the viva examination. The final award for Oxford based students will be a DPhil awarded by the University of Oxford.

Graduate destinations

This is a new course and there are no alumni yet. StatML is dedicated to providing the organisation, environment and personnel needed to develop the future industrial and academic individuals doing world-leading research in statistics for modern day science, engineering and commerce, all exemplified by ‘big data’.

Changes to this course and your supervision

The University will seek to deliver this course in accordance with the description set out in this course page. However, there may be situations in which it is desirable or necessary for the University to make changes in course provision, either before or after registration. The safety of students, staff and visitors is paramount and major changes to delivery or services may have to be made in circumstances of a pandemic, epidemic or local health emergency. In addition, in certain circumstances, for example due to visa difficulties or because the health needs of students cannot be met, it may be necessary to make adjustments to course requirements for international study.

Where possible your academic supervisor will not change for the duration of your course. However, it may be necessary to assign a new academic supervisor during the course of study or before registration for reasons which might include illness, sabbatical leave, parental leave or change in employment.

For further information please see our page on changes to courses and the provisions of the student contract regarding changes to courses.

Entry requirements for entry in 2024-25

Proven and potential academic excellence.

The requirements described below are specific to this course and apply only in the year of entry that is shown. You can use our interactive tool to help you  evaluate whether your application is likely to be competitive .

Please be aware that any studentships that are linked to this course may have different or additional requirements and you should read any studentship information carefully before applying. 

Degree-level qualifications

As a minimum, applicants should hold or be predicted to achieve the following UK qualifications or their equivalent:

• a first-class or strong upper second-class undergraduate degree with honours in mathematics, statistics, physics, computer science, engineering or a closely related subject. 

However, entrance is very competitive and most successful applicants have a first-class degree or the equivalent.

For applicants with a degree from the USA, the minimum GPA sought is 3.6 out of 4.0.

If your degree is not from the UK or another country specified above, visit our International Qualifications page for guidance on the qualifications and grades that would usually be considered to meet the University’s minimum entry requirements.

GRE General Test scores

No Graduate Record Examination (GRE) or GMAT scores are sought.

Other qualifications, evidence of excellence and relevant experience 

Publications are not expected but details of any publications may be included with the application.

English language proficiency

This course requires proficiency in English at the University's  standard level . If your first language is not English, you may need to provide evidence that you meet this requirement. The minimum scores required to meet the University's standard level are detailed in the table below.

*Previously known as the Cambridge Certificate of Advanced English or Cambridge English: Advanced (CAE) † Previously known as the Cambridge Certificate of Proficiency in English or Cambridge English: Proficiency (CPE)

Your test must have been taken no more than two years before the start date of your course. Our Application Guide provides further information about the English language test requirement .

Declaring extenuating circumstances

If your ability to meet the entry requirements has been affected by the COVID-19 pandemic (eg you were awarded an unclassified/ungraded degree) or any other exceptional personal circumstance (eg other illness or bereavement), please refer to the guidance on extenuating circumstances in the Application Guide for information about how to declare this so that your application can be considered appropriately.

You will need to register three referees who can give an informed view of your academic ability and suitability for the course. The  How to apply  section of this page provides details of the types of reference that are required in support of your application for this course and how these will be assessed.

Supporting documents

You will be required to supply supporting documents with your application. The  How to apply  section of this page provides details of the supporting documents that are required as part of your application for this course and how these will be assessed.

Performance at interview

Interviews are held as part of the admissions process for applicants who, on the basis of their written application, best meet the selection criteria.

Interviews may be held in person or over video link such as Zoom, normally with at least two interviewers. Interviews will include some technical questions on statistical topics relating to the StatML CDT. These questions will be adapted as far as possible to the applicant's own background training in statistics or machine learning.

How your application is assessed

Your application will be assessed purely on your proven and potential academic excellence and other entry requirements described under that heading.

References  and  supporting documents  submitted as part of your application, and your performance at interview (if interviews are held) will be considered as part of the assessment process. Whether or not you have secured funding will not be taken into consideration when your application is assessed.

An overview of the shortlisting and selection process is provided below. Our ' After you apply ' pages provide  more information about how applications are assessed . 

Shortlisting and selection

Students are considered for shortlisting and selected for admission without regard to age, disability, gender reassignment, marital or civil partnership status, pregnancy and maternity, race (including colour, nationality and ethnic or national origins), religion or belief (including lack of belief), sex, sexual orientation, as well as other relevant circumstances including parental or caring responsibilities or social background. However, please note the following:

• socio-economic information may be taken into account in the selection of applicants and award of scholarships for courses that are part of  the University’s pilot selection procedure  and for  scholarships aimed at under-represented groups ;
• country of ordinary residence may be taken into account in the awarding of certain scholarships; and
• protected characteristics may be taken into account during shortlisting for interview or the award of scholarships where the University has approved a positive action case under the Equality Act 2010.

Processing your data for shortlisting and selection

Information about  processing special category data for the purposes of positive action  and  using your data to assess your eligibility for funding , can be found in our Postgraduate Applicant Privacy Policy.

Admissions panels and assessors

All recommendations to admit a student involve the judgement of at least two members of the academic staff with relevant experience and expertise, and must also be approved by the Director of Graduate Studies or Admissions Committee (or equivalent within the department).

Admissions panels or committees will always include at least one member of academic staff who has undertaken appropriate training.

Other factors governing whether places can be offered

The following factors will also govern whether candidates can be offered places:

• the ability of the University to provide the appropriate supervision for your studies, as outlined under the 'Supervision' heading in the  About  section of this page;
• the ability of the University to provide appropriate support for your studies (eg through the provision of facilities, resources, teaching and/or research opportunities); and
• minimum and maximum limits to the numbers of students who may be admitted to the University's taught and research programmes.

Offer conditions for successful applications

If you receive an offer of a place at Oxford, your offer will outline any conditions that you need to satisfy and any actions you need to take, together with any associated deadlines. These may include academic conditions, such as achieving a specific final grade in your current degree course. These conditions will usually depend on your individual academic circumstances and may vary between applicants. Our ' After you apply ' pages provide more information about offers and conditions . 

In addition to any academic conditions which are set, you will also be required to meet the following requirements:

Financial Declaration

If you are offered a place, you will be required to complete a  Financial Declaration  in order to meet your financial condition of admission.

Disclosure of criminal convictions

In accordance with the University’s obligations towards students and staff, we will ask you to declare any  relevant, unspent criminal convictions  before you can take up a place at Oxford.

In January 2016 the Department of Statistics moved to occupy a newly-refurbished building in St Giles, near the centre of Oxford. The building has spaces for study and collaborative learning, including the library and large interaction and social area on the ground floor, as well as an open research zone on the second floor.

You will be provided with a computer and desk space in a shared office. You will have access to the Department of Statistics computing facilities and support, the department’s library, the Radcliffe Science Library and other University libraries, centrally-provided electronic resources and other facilities appropriate to your research topic. The provision of other resources specific to your DPhil project should be agreed with your supervisor as a part of the planning stages of the agreed project.

Tea and coffee facilities are provided in the Department. There are also opportunities for sporting interaction such as football and cricket.

The University's Department of Statistics is a world leader in research in probability, bioinformatics, mathematical genetics and statistical methodology, including computational statistics, machine learning and data science. 

You will be actively involved in a vibrant academic community by means of seminars, lectures, journal clubs, and social events. Research students are offered training in modern probability, stochastic processes, statistical methodology, computational methods and transferable skills, in addition to specialised topics relevant to specific application areas.

Much of the research in the Department of Statistics is either explicitly interdisciplinary or draws motivation from application areas, ranging from genetics, immunoinformatics, bioinformatics and cheminformatics, to finance and the social sciences.

The department is located on St Giles, in a building providing excellent teaching facilities and creating a highly visible centre for statistics in Oxford. Oxford’s Mathematical Sciences submission came first in the UK on all criteria in the 2021 Research Excellence Framework (REF).

View all courses   View taught courses View research courses

We expect that the majority of applicants who are offered a place on this course will also be offered a fully-funded scholarship specific to this course, covering course fees for the duration of their course and a living stipend.

For further details about searching for funding as a graduate student visit our dedicated Funding pages, which contain information about how to apply for Oxford scholarships requiring an additional application, details of external funding, loan schemes and other funding sources.

Please ensure that you visit individual college websites for details of any college-specific funding opportunities using the links provided on our college pages or below:

Please note that not all the colleges listed above may accept students on this course. For details of those which do, please refer to the College preference section of this page.

Annual fees for entry in 2024-25

Full-time study.

Further details about fee status eligibility can be found on the fee status webpage.

Part-time study

Information about course fees.

Course fees are payable each year, for the duration of your fee liability (your fee liability is the length of time for which you are required to pay course fees). For courses lasting longer than one year, please be aware that fees will usually increase annually. For details, please see our guidance on changes to fees and charges .

Course fees cover your teaching as well as other academic services and facilities provided to support your studies. Unless specified in the additional information section below, course fees do not cover your accommodation, residential costs or other living costs. They also don’t cover any additional costs and charges that are outlined in the additional information below.

Continuation charges

Following the period of fee liability , you may also be required to pay a University continuation charge and a college continuation charge. The University and college continuation charges are shown on the Continuation charges page.

Where can I find further information about fees?

The Fees and Funding  section of this website provides further information about course fees , including information about fee status and eligibility  and your length of fee liability .

Additional information

There are no compulsory elements of this course that entail additional costs beyond fees (or, after fee liability ends, continuation charges) and living costs. However, please note that, depending on your choice of research topic and the research required to complete it, you may incur additional expenses, such as travel expenses, research expenses, and field trips. You will need to meet these additional costs, although you may be able to apply for small grants from your department and/or college to help you cover some of these expenses.

Please note that you are required to attend in Oxford for a minimum of 30 days each year, and you may incur additional travel and accommodation expenses for this. Also, depending on your choice of research topic and the research required to complete it, you may incur further additional expenses, such as travel expenses, research expenses, and field trips. You will need to meet these additional costs, although you may be able to apply for small grants from your department and/or college to help you cover some of these expenses.

Living costs

In addition to your course fees, you will need to ensure that you have adequate funds to support your living costs for the duration of your course.

For the 2024-25 academic year, the range of likely living costs for full-time study is between c. £1,345 and £1,955 for each month spent in Oxford. Full information, including a breakdown of likely living costs in Oxford for items such as food, accommodation and study costs, is available on our living costs page. The current economic climate and high national rate of inflation make it very hard to estimate potential changes to the cost of living over the next few years. When planning your finances for any future years of study in Oxford beyond 2024-25, it is suggested that you allow for potential increases in living expenses of around 5% each year – although this rate may vary depending on the national economic situation. UK inflationary increases will be kept under review and this page updated.

If you are studying part-time your living costs may vary depending on your personal circumstances but you must still ensure that you will have sufficient funding to meet these costs for the duration of your course.

Students enrolled on this course will belong to both a department/faculty and a college. Please note that ‘college’ and ‘colleges’ refers to all 43 of the University’s colleges, including those designated as societies and permanent private halls (PPHs). 

If you apply for a place on this course you will have the option to express a preference for one of the colleges listed below, or you can ask us to find a college for you. Before deciding, we suggest that you read our brief  introduction to the college system at Oxford  and our  advice about expressing a college preference . For some courses, the department may have provided some additional advice below to help you decide.

The following colleges accept students for full-time study on this course:

• Balliol College
• Corpus Christi College
• Exeter College
• Hertford College
• Jesus College
• Keble College
• Kellogg College
• Lady Margaret Hall
• Linacre College
• Mansfield College
• New College
• Reuben College
• St Cross College
• St Edmund Hall
• Worcester College

The following colleges accept students for part-time study on this course:

Before you apply

Our  guide to getting started  provides general advice on how to prepare for and start your application. You can use our interactive tool to help you  evaluate whether your application is likely to be competitive .

If it's important for you to have your application considered under a particular deadline – eg under a December or January deadline in order to be considered for Oxford scholarships – we recommend that you aim to complete and submit your application at least two weeks in advance . Check the deadlines on this page and the  information about deadlines  in our Application Guide.

Application fee waivers

An application fee of £75 is payable per course application. Application fee waivers are available for the following applicants who meet the eligibility criteria:

• applicants from low-income countries;
• refugees and displaced persons; 
• UK applicants from low-income backgrounds; and 
• applicants who applied for our Graduate Access Programmes in the past two years and met the eligibility criteria.

You are encouraged to  check whether you're eligible for an application fee waiver  before you apply.

Readmission for current Oxford graduate taught students

If you're currently studying for an Oxford graduate taught course and apply to this course with no break in your studies, you may be eligible to apply to this course as a readmission applicant. The application fee will be waived for an eligible application of this type. Check whether you're eligible to apply for readmission .

Application fee waivers for eligible associated courses

If you apply to this course and up to two eligible associated courses from our predefined list during the same cycle, you can request an application fee waiver so that you only need to pay one application fee.

The list of eligible associated courses may be updated as new courses are opened. Please check the list regularly, especially if you are applying to a course that has recently opened to accept applications.

Do I need to contact anyone before I apply?

Before submitting an application, you may find it helpful to contact a potential supervisor or supervisors from among the online profile of StatML academics based in Oxford. This will allow you to discuss the matching of your interests with those of the centre, although there is no guarantee that this specific individual will become your supervisor if you are accepted. Please ensure that you have researched the specialisms of the department and those of your potential supervisor(s) before making contact. More information can be found on the  StatML website .

You can either contact the academic staff member directly or route your enquiry via the Admissions Administrator using the contact details provided on this page.

Completing your application

You should refer to the information below when completing the application form, paying attention to the specific requirements for the supporting documents .

For this course, the application form will include questions that collect information that would usually be included in a CV/résumé. You should not upload a separate document. If a separate CV/résumé is uploaded, it will be removed from your application .

If any document does not meet the specification, including the stipulated word count, your application may be considered incomplete and not assessed by the academic department. Expand each section to show further details.

You will also need to  complete the declaration form  once you have applied for this course.  

Proposed field and title of research project

Proposed supervisor.

Under 'Proposed supervisor name' enter the name of the academic(s) who you would like to supervise your research. 

Referees: Three overall, academic preferred

Whilst you must register three referees, the department may start the assessment of your application if two of the three references are submitted by the course deadline and your application is otherwise complete. Please note that you may still be required to ensure your third referee supplies a reference for consideration.

Your references should generally be academic, though up to one professional reference will be accepted.

Your references will support intellectual ability, academic achievement, motivation and your ability to work in a group.

Official transcript(s)

Your transcripts should give detailed information of the individual grades received in your university-level qualifications to date. You should only upload official documents issued by your institution and any transcript not in English should be accompanied by a certified translation.

More information about the transcript requirement is available in the Application Guide.

Statement of purpose/personal statement: A maximum of 1,100 words

Your statement should be written in English and should specify the broad areas in which your research interests lie -- what motivates your interest in these fields, and why do you think you will succeed in the programme?

The personal statement should describe your academic and career plans, as well your motivation and your scientific interests. When writing your personal statement, please make sure it answers the following questions:

• What are your machine learning/statistical interests?
• Why do you think the Statistics and  Machine Learning CDT is the right choice for you?

If possible, please ensure that the word count is clearly displayed on the document.

Your statement will be assessed for:

• your reasons for applying
• evidence of understanding of the proposed area of study
• your ability to present a coherent case in proficient English
• your commitment to the subject, beyond the requirements of the degree course
• your preliminary knowledge of the subject area and research techniques
• your capacity for sustained and intense work
• your reasoning ability
• your ability to absorb new ideas, often presented abstractly, at a rapid pace.

Start or continue your application

You can start or return to an application using the relevant link below. As you complete the form, please  refer to the requirements above  and  consult our Application Guide for advice . You'll find the answers to most common queries in our FAQs.

As the admissions process for StatML will be run in parallel with Imperial College London, we ask that you please  complete the declaration form once you have applied to one or both of the institutions.

Application Guide   Apply - FT   Apply - PT   Declaration Form

ADMISSION STATUS

Open - applications are still being accepted

Up to a week's notice of closure will be provided on this page - no other notification will be given

12:00 midday UK time on:

Friday 1 March 2024 Applications may remain open after this deadline if places are still available - see below

A later deadline shown under 'Admission status' If places are still available,  applications may be accepted after 1 March . The 'Admissions status' (above) will provide notice of any later deadline.

*Three-year average (applications for entry in 2021-22 to 2023-24)

This course was previously known as Modern Statistics and Statistical Machine Learning 

Further information and enquiries

This course is offered by the University's Department of Statistics , in partnership with Imperial College London

• Course page on the centre's website
• Funding information from the centre
• Academic and research staff  (incl. Imperial)
• Departmental research in Oxford
• Mathematical, Physical and Life Sciences
• Residence requirements for full-time courses
• Postgraduate applicant privacy policy

Course-related enquiries

Advice about contacting the department can be found in the How to apply section of this page

✉ [email protected] ☎ +44 (0)1865 272876  (Oxford)

Application-process enquiries

See the application guide

Visa eligibility for part-time study

We are unable to sponsor student visas for part-time study on this course. Part-time students may be able to attend on a visitor visa for short blocks of time only (and leave after each visit) and will need to remain based outside the UK.

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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Oxford Applied and Theoretical Machine Learning Group

How to Apply

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• Master's in Computer Science or a related field (or a four-year undergraduate degree)
• Good communication and presentation skills in English
• Knowledge and experience with the stuff you want to work on
• Good mathematical or engineering background is preferred
• specific to the topic (i.e. avoid stuff like "how can we solve AI"),
• measurable (how would we know that we managed to answer the question? e.g. if you want to improve autonomous driving safety in out-of-distribution, what metric would we use?), and
• achievable (even though it might require a few years of work to answer!).
• previous education (grades, course and university) and research work that you've done (e.g. published papers),
• relevant internships or industry experience,
• topics of interest to you and how they fit with the lab's wider research interests (see above for advice on how to write a proposal)
• Postdoc applicants : If you have a strong track record (either coming from machine learning or other fields) and would like to do a postdoc in machine learning, please email [email protected]. Also please note the funding opportunity with the Schmidt AI in Science Postdoctoral Fellowship Program .
• Internships : We do not accept interns at the moment. If we do in the future, it will be advertised here.

Computer Science Research PhD

Key information.

The Department of Informatics has an extensive research profile, with major externally funded projects, a strong publication profile and significant research activity.

Our research is organised around our research groups, and you can find details of the range of current research projects and interests on the Department's research pages .

If you are interested in joining us to undertake PhD research, you should identify topics and academic staff in your area of interest. If you cannot find your chosen topic or area on our individual research section or subgroup pages, contact a relevant member of academic staff for further information and then follow the application procedure.

Current number of academic staff: 79

Current number of research staff: 37

Head of department: Professor Luc Moreau

Course intake: Approximately 25-30 per year

Research income

Currently, the Department attracts approximately £4m in research funding annually.

Recent publications

All academics in the Department publish regularly, with well over 100 publications per year.

Partner organisations

We have strong links with industry, government and other academic institutions. Our research has been supported by several companies from the aerospace, automotive, financial, IT and telecommunications sectors.

Recent events

We host several workshops and conferences and other regular research meetings. Please check our website for forthcoming events.

• How to apply
• Fees or Funding

For funding opportunities please explore these pages:

• List of funding opportunities
• External funding opportunities for International students
• King’s-China Scholarship Council PhD Scholarship programme (K-CSC)

UK Tuition Fees 2023/24

Full time tuition fees: £6,540 per year

Part time tuition fees: £3,270 per year

International Tuition Fees 2023/24

Full time tuition fees: £28,260 per year

Part time tuition fees: £14,130 per year

UK Tuition Fees 2024/25

Full time tuition fees: £6,936 per year

Part time tuition fees: £3,468 per year

International Tuition Fees 2024/25

Full time tuition fees: £30,240 per year

Part time tuition fees: £15,120 per year

These tuition fees may be subject to additional increases in subsequent years of study, in line with King's terms and conditions.

• Study environment

We are a department with many internationally recognised researchers and visiting academics, large groups of PhD students, research assistants, national and international projects, collaborations with other departments as well as links with industry. We offer an exciting environment and excellent opportunities for research.

Our PhD students have access to good library facilities, designated PhD offices within the Department where PhD students can dock an assigned laptop for use throughout their studies, Regular group seminars are organised providing PhD students chance to showcase their research and receive feedback from academic staff and peers, and college-based training in transferable and research skills.

The Department is located on the Strand Campus, in the heart of central London, close to the cultural activities of the West End and the South Bank, to the major departments of state at Whitehall, and to the leading financial institutions of the City, and within easy reach of major transport links. Our facilities are within easy reach of the British Computer Society and the Institute of Engineering & Technology (and the IET Library), with access to a formidable collection of scientific journals and other technical material.

The Department moved to the historic Bush House in the summer of 2017, featuring state-of-the-art teaching and office spaces. Although the Department is fairly large in size, there is a friendly and inclusive culture, with regular social and celebratory events to bring staff and students together. Our staff and students come from all over the world, which provides a rich environment for teaching and research. Diversity is positively encouraged - find out more about the work we’re doing to ensure an inclusive and supportive working environment.

The scope of our research is defined by the interests of our research groups.

Postgraduate training

Faculty and College induction courses are scheduled at the beginning of your degree to prepare you for life as a PhD student. All students are required to complete 10 days of training each year. There is a centrally provided programme of related and transferable skills training coordinated by the Centre for Doctoral Studies .

Research students are also encouraged to submit papers to conferences, and we try to provide financial support for them to travel to present their papers.

Our research students are also encouraged to teach alongside their studies to help prepare them for a potential future career in academia.

• Entry requirements
• Research groups

Algorithms and Data Analysis

The group develops algorithmic solutions and concrete implementations for various applications.

Cybersecurity

The group studies design, modelling, analysis, verification and testing of networks and systems.

Distributed Artificial Intelligence

The group explores the use of AI in social and economic contexts where an intelligent entity may be interacting with other entities.

Human Centred Computing Research

The group is concerned with the design, development and evaluation of human computer systems.

Reasoning and Planning

The group focuses on the fundamental AI challenge of creating, representing and reasoning.

Software Systems

The group studies design, modelling and engineering of software systems.

Centre for Doctoral Studies

NMES Graduate School

A supportive and engaging environment for PhD students

Funding & Scholarships for PhD students

The Centre for Doctoral Studies helps secure funding for students...

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• School of Electronic Engineering and Computer Science
• PhD studentships

PhD Studentship in Machine Learning on Graphs

Applications are invited for a fully-funded PhD Studentship starting in September 2021 to undertake research in the area of machine learning on graphs.

Funding is for three years, covering student fees and, in addition, a tax-free stipend starting at £17,609 per annum. Applications are welcomed from candidates of all nationalities.

Graphs are commonly used as a natural representation for data that is best described in terms of its structure, such as chemical compounds, social interactions, and transportation networks. However, graph data brings together with its higher representational power a number of challenges that are not experienced when handling vectorial data. In machine learning, such challenges were tackled with the introduction of graph kernels and more recently graph neural networks. Several open problems exist in both areas, from the need of graph kernel ensembles that can exploit the wealth of kernels developed in these years, to the augmentation of graph data in the context of neural networks. The successful applicant will work in this exciting area to develop new techniques that will advance our ability to operate on graph data.

Applicants should have, or be expected to obtain by the start date, a 1st class or 2:1 degree (or equivalent) in Computer Science or a related subject.

The student will be supervised by Dr Luca Rossi and will be based in the School of Electronic Engineering and Computer Science at the University’s main Mile End campus in East London. Depending on the exact research direction, the student will be participate in either the Networks, Game AI, or Computer Vision group. Queen Mary is a leading research-intensive Russell Group university, ranked 5th among multi-faculty institutions in the UK for research outputs (Research Excellence Framework 2014), and 110th in the world overall (Times Higher Education World University Rankings 2020).

Informal enquiries regarding the post may be made by email to Dr Luca Rossi:   [email protected]

Applications should be made by following the online process at:  https://www.qmul.ac.uk/postgraduate/research/subjects/electronic-engineering.html.  (“PhD Full-time Electronic Engineering – Semester 1 (September Start)”).

The closing date for applications is Thursday 1st April 2021.

Interviews are expected to take place in the week beginning Monday 12th April 2021.

Student News

Wednesday, 17 april, search for news, browse student news stories.

• All Student news
• All featured

Fully Funded PhDs in Data Science, AI and Machine Learning

The University of Liverpool’s Centre for Doctoral training in Distributed Algorithms (CDT) are currently looking for students to join their fully funded PhDs in Data Science, AI and Machine Learning.

The team aim to develop 60 PhD students to meet the world’s pressing need for highly-trained data scientists and work with industry and government to help solve real-world problems.

Applicants come from a range of subjects and backgrounds, including:

• Computer Science
• Department of Civil Engineering and Industrial Design
• Earth, Ocean and Ecological Sciences
• Electrical Engineering and Electronics
• Geography and Planning
• Mathematical Sciences
• Mechanical and Aerospace Engineering

The fully funded PhD studentships are open to home and international students. You’ll be working as part of a cohort in a collaborative environment alongside other PhD students, postdoc researchers and data scientists. Other benefits include:

•  PhD projects co-defined and co-supervised with a project partner
•  Monthly tax-free payment of £1,338.50
•  Annual research grant
•  Placements in year 3
•  Long-term employment potential
•  Inclusive and supportive cohort environment
•  Technical, professional and personal training and development
•  Access to state-of-the-art high-performance computers

Interested?

The team would love to hear from you. Please do get in touch to find out more.

Email Kelli or Sara ( [email protected] ) if you have any questions.

They will also be at the Careers Studio on Friday 22 July between 11am – 1pm – drop-in to speak to the team, no appointment necessary.

Click here to find out more and apply

Further reading

• Click here to discover what our current students are working on and who with.
• Email one of our Student Ambassadors here and arrange a call with them.
• View our applicant information and guidance.
• Simon Maskell, CDT Director Bio
• Student Featured 2
• Careers and Employability
• CDT in Distributed Algorithms.
• Centre for Doctoral Training in Distributed Algorithms

Gatsby Computational Neuroscience Unit

PhD programme

• Programme structure
• Applications

Application for 2024 entry is now closed.

Phd in theoretical neuroscience and machine learning.

The four-year PhD programme includes in its first year intensive courses that provide a comprehensive introduction to theoretical and systems neuroscience and machine learning (see Teaching ). Multidisciplinary training in other areas of neuroscience is also available. We offer a supportive and interdisciplinary environment with close links to the Sainsbury Wellcome Centre for Neural Circuits and Behaviours (SWC) and the ELLIS Unit at UCL. Students are strongly encouraged to work and interact closely with peers and faculty at SWC and the ELLIS Unit to benefit from this uniquely multidisciplinary research environment. Projects involving collaboration with researchers at and/or external to UCL are welcome. For details see programme structure . Students study toward a PhD in either machine learning or computational and theoretical neuroscience, with minor emphasis in the complementary field. Exceptionally, some students with pre-secured studentships have joined us to study for an MPhil degree in one of these fields. Students from other PhD programmes can also carry out all or part of their research in the unit. We do not offer taught undergraduate and Master's degree programmes, nor a research Master's degree programme.

Around 90% of our alumni  students and postdoctoral fellows work in a scientific setting, with over 60% holding an academic position and about 30% working in research development in companies such as Google DeepMind and Facebook.

For more information on our current PhD students, please visit the People page and their individual pages linked therein.

Entry requirements

Applicants must have a strong analytical and mathematical background, a keen interest in neuroscience and/or machine learning, and a relevant first degree (for example, in Computer Science, Engineering, Physics, Statistics, Mathematics, Neuroscience, or Cognitive Psychology). Students seeking to combine work in neuroscience and machine learning are particularly encouraged to apply. Please note that candidates offered a place on the Gatsby PhD programme will be required to meet UCL's standard admissions requirements (including the English language requirements for international applicants).

Studentships

Full funding is available to all students, regardless of nationality. Our PhD studentships cover UCL tuition fees for both home and international students and include an annual tax-free stipend as well as travel budget for attending conferences and workshops. We also welcome applications from students with pre-secured funding or who are currently applying for other scholarships/studentships.

If you have additional questions, please see our FAQs or contact us .

AI and Machine Learning will not save the planet (yet)

AI isn't developed enough to save the environment

Artificial General Intelligence, when it exists, will be able to do many tasks better than humans. For now, the machine learning systems and generative AI solutions available on the market are a stopgap to ease the cognitive load on engineers, until machines which think like people exist.

Generative AI is currently dominating headlines, but its backbone, neural networks, have been in use for decades. These Machine Learning (ML) systems historically acted as cruise control for large systems that would be difficult to constantly maintain by hand. The latest algorithms also proactively respond to errors and threats, alerting teams and recording logs of unusual activity. These systems have developed further and can even predict certain outcomes based on previously observed patterns.

This ability to learn and respond is being adapted to all kinds of technology. One that persists is the use of AI tools in envirotech. Whether it's enabling new technologies with vast data processing capabilities, or improving the efficiency of existing systems by intelligently adjusting inputs to maximize efficiency, AI at this stage of development is so open ended it could theoretically be applied to any task.

Co-Founder of VictoriaMetrics.

AI’s undeniable strengths

GenAI isn’t inherently energy intensive. A model or neural network is no more energy inefficient than any other piece of software when it is operating, but the development of these AI tools is what generates the majority of the energy costs. The justification for this energy consumption is that the future benefits of the technology are worth the cost in energy and resources.

Some reports suggest many AI applications are ‘solutions in search of a problem’, and many developers are using vast amounts of energy to develop tools that could produce dubious energy savings at best. One of the biggest benefits of machine learning is its ability to read through large amounts of data , and summarize insights for humans to act on. Reporting is a laborious and frequently manual process, time saved reporting can be shifted to actioning machine learning insights and actively addressing business-related emissions.

Businesses are under increasing pressure to start reporting on Scope 3 emissions, which are the hardest to measure, and the biggest contributor of emissions for most modern companies. Capturing and analyzing these disparate data sources would be a smart use of AI, but would still ultimately require regular human guidance. Monitoring solutions already exist on the market to reduce the demand on engineers, so taking this a step further with AI is an unnecessary and potentially damaging innovation.

Replacing the engineer with an AI agent reduces human labor, but removes a complex interface, just to add equally complex programming in front of it. That isn’t to say innovation should be discouraged. It’s a noble aim, but do not be sold a fairy tale that this will happen without any hiccups. Some engineers will be replaced eventually by this technology, but the industry should approach it carefully.

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Consider self-driving cars. They're here, they're doing better than an average human-driver. But in some edge cases they can be dangerous. The difference is that it is very easy to see this danger, compared to the potential risks of AI.

Today’s ‘clever’ machines are like naive humans

AI agents at the present stage of development are comparable to human employees - they need training and supervision, and will gradually become out of date unless re-trained from time to time. Similarly, as has been observed with ChatGPT , models can degrade over time. The mechanics that drive this degradation are not clear, but these systems are delicately calibrated, and this calibration is not a permanent state. The more flexible the model, the more likely it can misfire and function suboptimally. This can manifest as data or concept drift, an issue where a model invalidates itself over time. This is one of many inherent issues with attaching probabilistic models to deterministic tools.

A concerning area of development is the use of AI in natural language inputs, trying to make it easier for less technical employees or decision makers to save on hiring engineers. Natural language outputs are ideal for translating the expert, subject specific outputs from monitoring systems, in a way that makes the data accessible for those who are less data literate. Despite this strength even summarizations can be subject to hallucinations where data is fabricated, this is an issue that persists in LLMs and could create costly errors where AI is used to summarize mission critical reports.

The risk is we create AI overlays for systems that require deterministic inputs. Trying to make the barrier to entry for complex systems lower is admirable, but these systems require precision. AI agents cannot explain their reasoning, or truly understand a natural language input and work out the real request in the way a human can. Moreover, it adds another layer of energy consuming software to a tech stack for minimal gain.

We can’t leave it all to AI

The rush to ‘AI everything’ is producing a tremendous amount of wasted energy, with 14,000 AI startups currently in existence, how many will actually produce tools that will benefit humanity? While AI can improve the efficiency of a data center by managing resources, ultimately that doesn't manifest into a meaningful energy saving as in most cases that free capacity is then channeled into another application , using any saved resource headroom, plus the cost of yet more AI powered tools.

Can AI help achieve sustainability goals? Probably, but most of the advocates fall down at the ‘how’ part of that question, in some cases suggesting that AI itself will come up with new technologies. Climate change is now an existential threat with so many variables to account for it stretches the comprehension of the human mind. Rather than tackling this problem directly, technophiles defer responsibility to AI in the hope it will provide a solution at some point in future. The future is unknown, and climate change is happening now. Banking on AI to save us is simply crossing our fingers and hoping for the best dressed up as neo-futurism.

We've listed the best collaboration platform for teams .

This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro

Roman Khavronenko, Co-Founder of VictoriaMetrics.

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A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases  Seminar

Event details.

Geography and Environmental Science Seminar

Modelling the spread of infectious diseases in space and time needs to take care of complex dependencies and uncertainties. Machine learning methods, and neural networks, in particular, are useful in modelling this sort of complex problems, although they generally lack probabilistic interpretations. We propose a neural network method embedded in a Bayesian framework for modelling and predicting the number of cases of infectious diseases in areal units. A key feature is that our combined model considers the impact of human movement on the spread of the infectious disease, as an additional random factor to the also considered spatial neighbourhood and temporal correlation components. Our model is evaluated over a COVID-19 dataset for 245 health zones of Castilla-Leon (Spain). The results show that a Bayesian model informed by a neural network method is generally able to predict the number of cases of COVID-19 in both space and time, with the human mobility factor having a strong influence on the model, together with the number of infections and deaths in nearby areas.

Speaker Information

Speaker: Somnath Chaudhuri

Somnath Chaudhuri is a Research Fellow specializing in spatial statistics in the WorldPop Research Group, University of Southampton, UK. His research interests include Bayesian models and spatiotemporal modelling across various domains such as socio-demographics, health, urban, and environmental areas. I obtained my Ph.D. in Spatial Statistics from the University of Girona, Spain.

He has a master's in computer science and engineering from the University of Calcutta, India. His second Masters in Geospatial Technology is under the Erasmus Mundus Scholarship Program jointly from three Universities of Germany, Spain and Portugal funded by the European Commission. He has several years of experience in academic and research roles at universities in different countries, including India, Bhutan, Maldives, Saudi Arabia, Germany, and Spain. 

Link to join in online

Please remember to turn off your camera/microphone to save bandwidth. 

Please also note that the seminar will be recorded.

Data Science & AI

Syllabi for MS/PhD Interview & Entrance Test

The written test will have two parts:

• Theory – These will be objective questions (MCQ, Fill in the blanks, True/False etc)
• Python Coding – 2 problems that you will be required to write a code for in Basic Python

Theory Syllabus

Probability and statistics.

– Counting (permutation and combinations) – independent events, mutually exclusive events – marginal, conditional and joint probability – Bayes Theorem – conditional expectation and variance – mean, median, mode and standard deviation – correlation, and covariance – random variables, discrete random variables and probability mass functions – uniform, Bernoulli, binomial distribution – Continuous random variables and probability – distribution function, cumulative distribution function, Conditional PDF – uniform, exponential, Poisson, normal, standard normal, t-distribution – chi-squared distributions – Central limit theorem – confidence interval – z-test, t-test,chi-squared test.

Linear Algebra

– Vector space, subspaces – linear dependence and independence of vectors – matrices, projection matrix, orthogonal matrix, idempotent matrix, partition matrix – quadratic forms – systems of linear equations and solutions – Gaussian elimination – eigenvalues and eigenvectors – determinant, rank, nullity – projections – LU decomposition, singular value decomposition.

Calculus and Optimization

– Functions of a single variable – limit, continuity and differentiability – Taylor series – maxima and minima – optimization involving a single variable.

Programming, Data Structures and Algorithms

– Programming in Python – Basic data structures: stacks, queues, linked lists, trees, hash tables – Search algorithms: linear search and binary search – Basic sorting algorithms: selection sort, bubble sort and insertion sort – Divide and conquer: mergesort, quicksort – Introduction to graph theory – Basic graph algorithms: traversals and shortest path

Coding Syllabus

You will be given some coding tasks that you need to complete and execute by writing Python scripts. To be able to do this you will need to know the following:

– Basic Python syntax – comments, variables, basic data types – Operators and Control Flow – If/else, for, while, range, break, continue, pass = Functions – How to define and use them – Lists/Arrays, Tuples, and associated methods

================================================================

Interview Topics

For those who qualify after the written test for the online interview, questions from the following additional topics may be asked during the interview

For MS/PhD Interviews

Machine learning.

– Supervised Learning regression and classification problems – Simple linear regression – Multiple linear regression – Ridge regression – Logistic regression – k-nearest neighbour – Naive Bayes classifier – Linear discriminant analysis – Support vector machine – Decision trees – Bias-variance trade-off – Cross-validation methods such as leave-one-out (LOO) cross-validation, k-folds cross-validation, multi-layer perceptron, feed-forward neural network – Unsupervised Learning: clustering algorithms

Artificial Intelligence (AI)

– Search: informed, uninformed, adversarial – Logic: Propositional Logic, Predicate Logic – Reasoning under Uncertainty Topics – Conditional Independence Representation – Exact Inference through Variable Elimination – Approximate Inference through Sampling

PhD applicants may also be asked questions from specialized topics for the interview – They can select a topic from Deep Learning, NLP, Vision, RL, Time-Series modeling depending on their interest and background.

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• Schools & departments

PhD studentship in "Machine Learning Theory & Economics"

Deadline: 5 January 2024 (or until the position is filled)

One fully funded, full-time PhD position to work with Dr Fengxiang He at the Artificial Intelligence and its Applications Institute (AIAI), School of Informatics, University of Edinburgh on a project titled “Machine Learning Theory & Economics”.

The aim of this project could be in deep learning theory, privacy in machine learning, theory of decentralised learning, symmetry in machine learning, learning theory in game-theoretical problems, and their applications in economics, such as auction, voting, resource allocation, and decentralised finance.

Candidate’s profile

• A Bachelor’s Hons degree (at class 2.1 or above, or international equivalent) and/or Master’s degree in a relevant subject (mathematics, statistics, economics, or related subject)
• A strong mathematical background, with an emphasis on analysis, algebra, geometry, differential equation, probability, and statistics. Recipients of mathematics competition medals are highly desirable
• Proficiency in English (both oral and written)
• Relevant research experiences in machine learning, statistics, economics, etc. are desirable but not necessary
• Programming skills in Python, PyTorch, TensorFlow, etc. are a plus but not necessary

Studentship and eligibility

The School funded studentship starting in the academic year 2023/24 covers:

• Full time PhD tuition fees for a student with a Home fee status (£4,712 per annum) and/or overseas fee status (£29,700 per annum)
• A tax-free stipend of £18,622 per year for 3.5 years
• Additional programme costs of £1,000 per year

Application information

Applicants should apply via the University’s admissions portal (EUCLID) and apply for the following programme: AIAI: Foundations and Applications of Artificial Intelligence, Automated Reasoning, Agents, Data Intensive Research .

Applicants should select a start date to start their application, as follows:

• 1 May 2024 (Home applicants only)
• 1 September 2024 (Home and International applicants)

Applicants should state “ Machine Learning Theory & Economics ” and the research supervisor ( Dr Fengxiang He ) in their application and Research Proposal document.

Complete applications submitted by 5 January 2023 will receive full consideration; after that date applications will be considered until the position is filled. The anticipated start date is 1 May 2024 , 1 September 2024 , or later depending on immigration requirements of the successful applicant.

Applicants must submit:

• All degree transcripts and certificates (and certified translations if applicable)
• Evidence of English Language capability (where applicable)
• A short research proposal (max 2 pages)
• A full CV and cover letter describing your background, suitability for the PhD, and research interests (max 2 pages)
• Two references (note that it the applicant’s responsibility to ensure reference letters are received before the deadline)

Only complete applications (i.e., those that are not missing the above documentation) will progress forward to Academic Selectors for further consideration.

Applicants are highly encouraged to contact Dr Fengxiang He  at [email protected] to discuss their cases before submissions.

Environment

The School of Informatics is one of the largest in Europe and currently the top Informatics institute in the UK for research power, with 40% of its research outputs considered world-leading (top grade), and almost 50% considered top grade for societal impact. The University of Edinburgh is constantly ranked among the world’s top universities and is a highly international environment with several centres of excellence.

The School of Informatics is exceptionally strong in the area of AI, hosting one of the largest groups for AI in the world. The successful applicant will be part of the Trustworthy AI & Economics group and will have the opportunity to interact with the other members of the group and more widely within the School of Informatics.

Dr Fengxiang He is Lecturer at Artificial Intelligence and its Application Institute, School of Informatics, University of Edinburgh. He received BSc in statistics from University of Science and Technology of China, MPhil and PhD from the University of Sydney. His research interest is trustworthy AI, with emphasis on deep learning theory and explainability, theory of decentralised learning, symmetry in machine learning, learning theory in game-theoretical problems, and their applications in economics and finance. He is a member of IEEE's Global Initiative on XR Ethics, AI/ML Terminology and Data Formats Working Group, Decentralized Metaverse Initiative, and Ethical Assurance of Data-Driven Technologies for Mental Healthcare. He is an Area Chair of UAI, AISTATS, and ACML. Please visit Fengxiang He's homepage for more information.

Dr Fengxiang He

This article was published on 2024-03-18