phd in machine learning in uk

• 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

Programme website: Centre for Doctoral Training in Machine Learning Systems

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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
• Programme: Centre for Doctoral Training in Machine Learning Systems
• 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 are encouraged to apply as soon as they are able as we will assess applications as they arrive. Ideally applications should be submitted by 31 May 2024, but we may assess applications received beyond that point if there remains unallocated funding.

• 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).

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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 and when to apply  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.

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Computer Science

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Key information.

Duration: 4 years full time or 5 years part time

Institution code: R72

Campus: Egham

UK fees * : £4,786

International/EU fees ** : £26,300

Computer Science at Royal Holloway is one of the world's leading centres of research in advanced areas of theoretical and applied computer science.

We are renowned in particular for our research excellence in Machine Learning, Artificial Intelligence, and Distributed Computing. Our research expertise is shaping the future of those key areas for society and the economy.

In the latest Research Excellence Frameworks (REF, 2021), we ranked 17th in the UK computer science departments for the quality of our research (THE, REF institutions ranked by subject, 2022).

Collaborative research is carried out in many different fields with companies and organisations, from finance to the analysis of movie scripts, from computational simulations of epidemics to foundational aspects of probability and logic.

We welcome applications for Postgraduate Research studies from across the world, and the department has earned a Bronze Athena SWAN award for best practice in equal opportunities and our inclusive environment.

In addition to the information below, check our separate page on the topics in which we supervise.

Research facilities and environment

Research students in Computer Science join a lively research community and become fully involved in the research activities of the department, sharing their successes. Many of our research students present papers at international conferences, with financial support from the department. 

A Postgraduate Colloquium is held annually: all second and third-year research students give 15-minute talks to present the results of their research achieved during the year to members of staff from different research groups, fellow research students, and external guests; first-year students give a poster presentation.

Regular departmental research seminars include talks by professionals in a variety of application areas, and there is a separate student-organised 'cakes talk' series which allows students to develop their presentational skills. We have a weekly social event to which all students, research assistants and staff are invited. Royal Holloway provides dedicated social space for graduate students, facilitating inter-departmental links. 

Major research topics include Algorithms and Complexity, Bioinformatics and Medical Applications, Machine Learning, Planning, Cyber-Physical Systems, Distributed Computing, and Type Theory and Applications.

Especially for topics that involve data analytics, the department has a dedicated state-of-the-art computing cluster HP DL380 comprising 32 processors, 32 Terabytes of disk storage, GPU and RDMA networking. The cluster is currently running the full-fledged Hortonworks Hadoop distribution (HDP) as well as the PostgreSQL and MongoDB servers.

You will have 24/7 access to dedicated on-site facilities and equipment.

Students who are admitted to this programme are initially registered on a MPhil programme. Before the end of their second year of studies they will have an upgrade meeting; if successful, they then become registered on the PhD Computer Science programme.

To be awarded a PhD Computer Science degree, students must submit their thesis, and pass their viva examination with one internal and one external examiner.

Entry requirements

A good first degree or masters in a cognate discipline to computer science, and relevant to the proposed research project. We are also prepared to consider candidates with extensive practical or industrial experience that would be relevant to the project. As a guideline, English proficiency equivalent to IELTS 6.5 is expected.

English language requirements

All teaching at Royal Holloway is in English. You will therefore need to have good enough written and spoken English to cope with your studies right from the start.

The scores we require

• IELTS: 6.5 overall. Writing 7.0. No other subscore lower than 5.5.
• Pearson Test of English: 61 overall. Writing 69. No other subscore lower than 51.
• Trinity College London Integrated Skills in English (ISE): ISE III.
• Cambridge English: Advanced (CAE) grade C.
• TOEFL ib: 88 overall, with Reading 18 Listening 17 Speaking 20 Writing 26.

Country-specific requirements

For more information about country-specific entry requirements for your country please see  here .

Our graduates have an excellent track record of finding employment.  PhD graduates from this department are now in senior positions in both academia and industry, including senior research managers at both Facebook and Amazon.

During term-time, we have weekly talks by external speakers from industry: these occasions are useful for graduate students to make external contacts and to learn of opportunities. In addition to the support provided by The Careers and Employability Service, the department has a coordinator to advise on career development and job opportunities.

Fees & funding

Home (UK) students tuition fee per year*: £4,786

EU and international students tuition fee per year**: £26,300

Other essential costs***: There are no single associated costs greater than £50 per item on this course

…How do I pay for it? Find out more about   funding options,   including loans, grants,   scholarships   and bursaries. 

* and ** These tuition fees apply to students enrolled on a full-time basis in the academic year 2024/25.

* Please note that for research courses, we adopt the minimum fee level recommended by the UK Research Councils for the Home   tuition fee. Each year, the fee level is adjusted in line with inflation (currently, the measure used is the Treasury GDP deflator). Fees displayed here are therefore subject to change and are usually confirmed in the spring of the year of entry.   For more information on the Research Council Indicative Fee please see the   UKRI website.

** This figure is the fee for EU and international students starting a degree in the academic year 2024/25.   

Royal Holloway reserves the right to increase all postgraduate tuition fees annually, based on the UK’s Retail Price Index (RPI). Please therefore be aware that tuition fees can rise during your degree (if longer than one year’s duration), and that this also means that the overall cost of studying the course part-time will be slightly higher than studying it full-time in one year. For further information, please see our  terms and conditions .

***   These estimated costs relate to studying this particular degree at Royal Holloway during the 2024/25 academic year and are included as a guide. Costs, such as accommodation, food, books and other learning materials and printing, have not been included. 

Explore Royal Holloway

Scholarships

Get help paying for your studies at Royal Holloway through a range of scholarships and bursaries.

Clubs and societies

There are lots of exciting ways to get involved at Royal Holloway. Discover new interests and enjoy existing ones.

Heading to university is exciting. Finding the right place to live will get you off to a good start.

Whether you need support with your health or practical advice on budgeting or finding part-time work, we can help.

Discover more about our academic departments and schools.

Research Excellence Framework

Find out why Royal Holloway is in the top 25% of UK universities for research rated ‘world-leading’ or ‘internationally excellent’.

Challenge-led research themes

Royal Holloway is a research intensive university and our academics collaborate across disciplines to achieve excellence.

Discover world-class research at Royal Holloway.

Discover more about who we are today, and our vision for the future.

Royal Holloway began as two pioneering colleges for the education of women in the 19th century, and their spirit lives on today.

We’ve played a role in thousands of careers, some of them particularly remarkable.

Find about our decision-making processes and the people who lead and manage Royal Holloway today.

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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• Centre for Machine Intelligence

The University of Sheffield offers various PhD programmes with different opportunities to study Artificial Intelligence.

PhD opportunities

Language Model-Grounded User Simulation for Personalised Conversational Systems

Department of Computer Science

This PhD project aims to advance the development of personalised conversational systems by leveraging user simulation, an area of research supported by recent advancements in large language models known for their strong capability in natural language understanding and generation.

Deadline: Saturday 1 June

Find out more information and apply h e re .  

Embodied Decisions in Animal and Robot Brains

Department of Automatic Control and Systems Engineering

This project will study mechanisms for making effective decisions to achieve goals that are embedded in space, drawing on inspiration from brains to design algorithms for robots.

Deadline:  Sunday 2 June

Collective Decisions in Animals and Robots

This project will study mechanisms for making effective collective group decisions to achieve goals that are embedded in space, drawing on inspiration from animal behaviour and neuroscience to design algorithms for robots.

AI for Multi-modal Healthcare

A 3.5-year funded PhD studentship is available in the Department of Computer Science at University of Sheffield, working on the topic of multi-modal AI for healthcare. This project will focus on developing multi-modal AI models that can handle the complexities of medical data as well as the domain gap and knowledge gap across different scenarios, and further adapt to individual patient needs.

Deadline:  Monday 10 June

Find out more information and apply here. 

Efficient and Robust Alignment of Large Language Models

In this PhD, the student will explore developing new state-of-the-art methods for efficient and robust alignment of LLMs. Directions include: (i) data efficient fine-tuning and preference optimization; (ii) robustness to distribution shifts; and (iii) model compression.

 AI-driven Geographic Data Integration

We are seeking a motivated PhD candidate to contribute to our efforts to develop innovative artificial intelligence-based tools for integrating and consolidating multidimensional geographic data from diverse sources.

Deadline:  Sunday 30 June

Find out more information and apply here.

Causal Representation Learning in Computer Vision

The primary objective of this research is to develop novel techniques for learning causal representations from visual data.

Energy-efficient AI Using Modular Deep Reservoir Computing

This project aims to explore how diverse properties in recurrent neural networks can be used to create reservoir computing architectures able to tackle challenging real-world tasks.

Applications accepted all year round.

Find out more information and apply here.  

Statistics and AI for Engineering and Smart Manufacturing

School of Mathematics and Statistics

We are thrilled to announce an exceptional PhD opportunity in the dynamic fields of Statistics and Artificial Intelligence, specifically tailored for Engineering and Smart Manufacturing. This project is designed for candidates eager to explore and expand the realms of statistical analysis and reliable AI in industrial pipelines, with a special emphasis on smart manufacturing and electronic design automation (EDA).

Find out more information and apply here . 

Artificial intelligence and machine learning methods for model discovery in the social sciences

Many theories have been proposed for how problematic social phenomena - such as segregation, inequality, and clustering of unhealthy behaviours - emerge and change over time. But which theories are useful for understanding these phenomena and devising effective interventions to change them? This project will develop computationally efficient model discovery methods to enable a search for explanatory multi-level agent-based models that can be calibrated to - and validated against - such empirical phenomena.

Online discussion; augmenting argumentation with chatbots

Department of Psychology

Argumentation - the systematic exchange of reasoning supporting or undermining an idea - enhances communication between individuals. Unreliable chains of thought are weeded out, reliable ones survive. Striking evidence for this is that reasonings tasks which provoke systematic errors when considered by individuals choice can be solved correctly by small groups, if they are given time for discussion. Chatbots with natural language processing create an opportunity to have artificial agents interact with group deliberation, and make it more effective. In this way, the strengths of human and artificial intelligence can augment each other

Machine Learning Digital Twins of Spintronic Neuromorphic Devices

This project will develop the use of machine learning methods, particularly neural differential equations, for predicting the dynamics of experimental systems designed for neuromorphic computing. 

Related information

Find out more about PhD study at the University of Sheffield

Postgraduate taught courses - AI

Search for PhD opportunities at Sheffield and be part of our world-leading research.

Oxford Applied and Theoretical Machine Learning Group

How to Apply

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• Postdoc applicants : 22 May 2024 Update: We are looking for two Postdocs to join OATML, working on Foundational AI Safety. You’ll lead and contribute to projects aimed at developing principled and practical safe AI methods which could be used in real systems. Check out linked roles here and here - closing date 12th June, 2024. Separate to these open roles, please note the funding opportunity with the Schmidt AI in Science Postdoctoral Fellowship Program .
• 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)
• Internships : We do not accept interns at the moment. If we do in the future, it will be advertised here.

King's College London

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

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MPhil/PhD Computer Science

Key Information

MPhil: 1.5-3 years PhD: 2-4 years

MPhil: 2-4 years PhD: 3-8 years

Entry Requirements

Brayford Pool

Start Dates in October and January

Programme Overview

This programme offers the opportunity to work alongside academic staff whose research has been internationally recognised. Researchers can benefit from a supportive community and environment in which to share ideas and experiences with the aim of advancing knowledge.

Research undertaken in the School includes advancements in computational neuroscience, biosignal analysis, machine learning, data analytics, robotics, medical imaging, AI in healthcare, games computing, VR/AR environments, human-computer interaction, agriculture, and food production.

Students can benefit from a comprehensive programme of training designed to develop key research skills and methodologies. Access is provided to industry-standard equipment and software to aid students' investigations, including VR-capable computers with headsets (e.g. HTC Vive and Oculus Rift), specialist development environments, and engineering design and simulation platforms. A supervisory team of experienced academics is available to provide guidance on students' studies and careers, and also to support publishing work in journals and presenting at conferences.

Key Features

Conduct independent, original, and academically significant research

Benefit from training courses to develop key research skills

Supervision and support from academic staff

Present at talks and seminars to showcase your work

Enrol in January or October each year

How You Study

Students on this programme usually work under the guidance of one main supervisor and one secondary supervisor throughout their studies, and will have access to a range of the School's facilities.

Training in research methods features in the early part of the programme and students are encouraged to present and publish their findings in national and international conferences and journals.

Due to the nature of postgraduate research programmes, the majority of time is spent in independent study and research. Students will have meetings with their academic supervisor, but the frequency of these will vary depending on individual requirements, subject area, staff availability, and stage of programme.

Research Centres, Groups, and Topics

The School of Computer Science undertakes a blend of fundamental, applied, and interdisciplinary research. There are particular strengths in computational neuroscience, machine learning, data analytics, robotics, medical imaging, AI in healthcare, and many aspects of human computer interaction and games computing.

The key to success on a postgraduate research programme is to find a research topic that you are passionate about and identify a supervisory team that has expertise in this area. The first thing that all prospective students should do is directly contact a member of staff from one of these areas that you feel is best aligned with your chosen research area to discuss the application process further. You can explore our research centres and groups below to find out more about our current research activity.

Interactive Technologies (intLab)

The interactive technologies lab (intLab) is a Human-Computer Interaction (HCI) research group. The group unites a broad range of members with expertise in computer science, psychology, and design.

Lincoln Centre for Autonomous Systems

L-CAS specialises in perception, learning, decision-making, control, and interaction for autonomous systems, such as robots.

Laboratory of Vision Engineering (LoVE)

This groups specialises in the capture, transmission, processing and understanding of image, video and other high-dimensional data.

Machine Learning

The MLearn group specialises in artificial and computational intelligence, focused on the design and development of machines that are able to reason, predict, and adapt to changing environments.

How you are assessed

A PhD is usually awarded based on the quality of a student's thesis, their publications in journals and international conferences, and their ability to present and successfully defend their chosen research topic in an oral examination (viva voce). They are also expected to demonstrate how their research findings have contributed to knowledge or expanded upon existing theory or understanding.

How to Apply

Postgraduate Research Application Support

Find out more about the application process for research degrees and what you'll need to complete on our How to Apply page, which also features contact details for dedicated support with your application.

Make an Enquiry

To find out more about postgraduate research in Computer Science, you can contact the programme leader, Dr Vassilis Cutsuridis. Dr Cutsuridis is an expert at the interface between AI and neuroscience and is interested in reverse engineering how the brain and mind work in order to understand the neural circuits and systems that give rise to mental experience, and to extract the neural algorithms for the design and development of more efficient intelligent methods and systems for complex data analysis.

To support your experience within the postgraduate research community, new students are encouraged to enrol in October or January. In addition to meeting peers across the University who are starting their research programme at the same time, there is access to a central training programme designed around the first three months of study, and targeted support aligned to each stage of the postgraduate research journey. Alternative enrolment dates may be agreed with your supervisor on an individual basis.

Entry Requirements 2024-25

First or second class honours degree in a relevant STEM subject.

If you have studied outside of the UK, and are unsure whether your qualification meets the above requirements, please visit our country pages for information on equivalent qualifications.

https://www.lincoln.ac.uk/home/studywithus/internationalstudents/entryrequirementsandyourcountry/

Overseas students will be required to demonstrate English language proficiency equivalent to IELTS 6.0 overall, with a minimum of 5.5 in each element. For information regarding other English language qualifications we accept, please visit the English Requirements page. https://www.lincoln.ac.uk/home/studywithus/internationalstudents/englishlanguagerequirementsandsupport/englishlanguagerequirements

If you do not meet the above IELTS requirements, you may be able to take part in one of our Pre-session English and Academic Study Skills courses. These specialist courses are designed to help students meet the English language requirements for their intended programme of study.

https://www.lincoln.ac.uk/home/studywithus/internationalstudents/englishlanguagerequirementsandsupport/pre-sessionalenglishandacademicstudyskills

If you are an overseas student, you may require an ATAS (Academic Technology Approval Scheme) certificate in order to enrol on this course. https://www.gov.uk/guidance/academic-technology-approval-scheme

Programme Fees

You will need to have funding in place for your studies before you arrive at the University. Our fees vary depending on the course, mode of study, and whether you are a UK or international student. You can view the breakdown of fees for this programme below. Research students may be required to pay additional fees in addition to cover the cost of specialist resources, equipment and access to any specialist collections that may be required to support their research project. These will be informed by your research proposal and will be calculated on an individual basis.

Funding Your Research

Loans and Studentships

Find out more about the optional available to support your postgraduate research, from Master's and Doctoral Loans, to research studentship opportunities. You can also find out more about how to pay your fees and access support from our helpful advisors.

Career Development

A doctoral qualification can be the capstone of academic achievement and often marks the beginning of a career in academia or research. A research programme provides the opportunity to become a true expert in your chosen field, while developing a range of valuable transferable skills that can support your career progression. A research-based degree is also the most direct pathway to an academic career. PhDs and research degrees are a great chance to expand your network and meet diverse people with similar interests, knowledge, and passion.

The University’s Doctoral School provides a focal point for Lincoln’s community of researchers, where ideas and experiences can be developed and shared across disciplines. It also offers support and training to help equip you for both academic and non-academic careers.

Doctoral School

Research at Lincoln

Through our research, we are striving to change society for the better. Working with regional, national, and international partners, our academics are engaged in groundbreaking studies that are challenging the status quo. We also understand the importance of providing the best possible environment for pursuing research that can support our communities and make a tangible difference to the world around us.

Prioritising Face-to-Face Teaching

At the University of Lincoln, we strive to ensure our students’ experience is engaging, supportive, and academically challenging. Throughout the Coronavirus pandemic, we have adapted to Government guidance to keep our students, staff, and community safe. All remaining Covid-19 legal restrictions in England were lifted in February 2022 under the Government’s Plan for Living with Covid-19, and we have embraced a safe return to in-person teaching on campus. Where appropriate, face-to-face teaching is enhanced by the use of digital tools and technology and may be complemented by online opportunities where these support learning outcomes.

We are fully prepared to adapt our plans if changes in Government guidance make this necessary, and we will endeavour to keep current and prospective students informed. For more information about how we are working to keep our community safe, please visit our coronavirus web pages .

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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

Friday, 31 may, search for news, browse student news stories.

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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

• Internal wiki

Cambridge – Tübingen PhD Fellowships in Machine Learning

The University of Cambridge Machine Learning Group and the Max Planck Institute for Intelligent Systems Empirical Inference Department in Tübingen are two of the world’s leading centres for machine learning research. In 2014, we launched a new and exciting initiative whereby a small group of select PhD candidates are jointly supervised at both institutions. The principal supervisors are Carl Rasmussen, Neil Lawrence, Ferenc Huszar, Jose Miguel Hernandez-Lobato, David Krueger, Adrian Weller, Hong Ge and Rika Antonova at Cambridge University, and Bernhard Schölkopf and other research group leaders at the Max Planck Institute in Tübingen. This program is specific for candidates whose research interests are well-matched to both the principal supervisors in Cambridge and the MPI for Intelligent Systems in Tuebingen.

This is a unique programme, and admission in the last years was highly competitive. 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 , https://mlatcl.github.io and http://ei.is.tuebingen.mpg.de

The overall duration of the PhD will be four years, with roughly three years spent at one location, and one year spent at the other location, including initial coursework at the University of Cambridge. Successful PhDs will be officially granted by the University of Cambridge.

Applications to the Cambridge – Tübingen PhD Fellowships should be made by first applying to the Cambridge PhD programme in Advanced Machine Learning as described here: https://mlg.eng.cam.ac.uk/phd_programme_in_advanced_machine_learning/

Applicants should also make sure that they apply for “Cambridge Trusts”, “Gates Cambridge”, and “Other Research Councils” funding by ticking the relevant boxes in the application form.

Applicants from Europe don’t need an employment visa, for those who apply from non-EU countries it is necessary to inform themselves at the embassy of their country about visa procedures.

Once you have completed the application to the Cambridge PhD Programme, and before actually submitting it, you should download the application and send a copy to [email protected] . It is important to download the application before submitting it through the academic portal because the application is no longer available once it is submitted. Please use the subject line “Cambridge-Tuebingen PhD fellowship” in your email to [email protected] . Please arrange for a cv, a motivation letter, BSc. and MSc. transcripts, and two reference letters of your application to the Cambridge PhD programme in Advanced Machine Learning to be sent also to [email protected] .

The hard deadline for formal applications is noon December 5, 2023

Interviews will take place in January 2024 .

The University of Cambridge is committed in its pursuit of academic excellence to equality of opportunity and to a proactive and inclusive approach to equality, which supports and encourages all under-represented groups, promotes an inclusive culture, and values diversity.

The Max-Planck Society is committed to increasing the number of individuals with disabilities in its workforce and therefore encourages applications from such qualified individuals. Furthermore, the Max Planck Society seeks to increase the number of women in those areas where they are underrepresented and therefore explicitly encourages women to apply.

We look forward to receiving your applications.

Carl Rasmussen

University of Cambridge

Bernhard Schölkopf

Max Planck Institute for Intelligent Systems

PhD Studentship: Physics informed machine learning for climate impacts on hydrology

The university of manchester - mechanical, aerospace and civil engineering.

Hydrological modelling has a long legacy of development and as such there are a huge range of models to choose from for practical use. One of the key differences between hydrological models is the level of physical representation of catchment processes within the model code. Machine learning (ML) models are entirely data-driven and contain no pre-conceived representation of catchment processes. Conceptual models represent processes in a simplified way that are parameterised and calibrated to observational data. Physically based (PB) models codify known physical laws into a single modelling framework. Each model structure has strengths and weaknesses. Recent studies have highlighted the superior performance of Machine Learning (ML) models over conceptual and PB models in replicating historical river flows, indicating their potential for more effective operational use, yet water managers remain wary of ML models due to their opaque nature, raising concerns about process visibility. Conversely, PB models offer explicit representation of known physical processes, and have been shown to simulate more robust projections of future river flows under climate change. Current operational methodologies predominantly rely on conceptual models, lacking the sophistication of more advanced techniques.

Emerging research suggests that hybrid ML and PB models hold promise for achieving even better historical simulations, with the added bonus of improved process understanding and robustness for use in climate impact studies. However, this area remains largely unexplored in the hydrological domain. This PhD opportunity therefore aims to explore the area of PB+ML hydrological modelling in the UK context and address several key research objectives:

• Investigate the reasons behind the superior performance of ML models over physically based models and how they leverage data more effectively.
• Explore whether ML models emulate physical processes absent in physically based models and if ML models can identify expressions of these processes.
• Assess the applicability of existing Physics-Informed Neural Network (PINN) and hybrid ML+PB models in hydrological contexts, determining the most appropriate framework.
• Evaluate whether a PB+ML model outperforms existing national models in the UK.
• Develop a national-scale PB+ML model for the UK and assess its confidence in predicting flows in a variety of catchments.
• Examine the ability of a PB+ML model to robustly project future changes in floods and droughts.
• Embark on this exciting journey to push the boundaries of hydrological modelling and contribute to solutions for pressing water management challenges. Apply now to be at the forefront of cutting-edge research in the field.

Eligibility

Applicants should have, or expect to achieve, an excellent academic record (UK First-class or 2.1 honours or international equivalent depending on the funding source) in Engineering, Earth Sciences, Computing or another related physical science discipline (MSc, MSci or BSc). You should have appropriate experience in hydrology, modelling or machine learning and an interest in developing your modelling skills. Some knowledge or previous experience in flood and drought management or computational modelling would be helpful, but is not an essential since you will receive training in all the relevant techniques. You will be encouraged to attend national and international conferences to share your research.

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Institute for Global Health

• About the Institute
• News & Events

PhD studentship available in Pharmacometrics and Machine Learning

24 May 2024

A four-year PhD Studentship in Pharmacometrics and Machine Learning funded by the UKRI EPSRC and GSK is available within the Institute for Global Health.

The studentship will commence from 1st October 2024 onwards, under the supervision of Dr Frank Kloprogge with subsidiary supervision from Prof Joseph Standing and Nuria Buil-Bruna

Project Title: Towards efficient drug development modelling with machine learning.

A large proportion of expensive Phase III trials fail. In recent years Phase III failure has declined, in part due to the integration of model-informed decision making in earlier phases. Pharmacometric (pharmacokinetic/pharmacodynamic (PK-PD)) models are used at all stages of pre-clinical and clinical development, but they are based on mathematical and statistical principles dating from the 1970s. Developing these pharmacometric models remains a laborious task where highly qualified staff spend large amounts of time.

The overarching aim is to enhance drug effect understanding through improved PK/PD predictions using Machine Learning (ML) and leveraging standardised and centralised big data. The distinctive feature is the integration of nonlinear mixed effects (or multi-level) modelling of time-series (repeated measure) data in combination with ML and prior distributions, something that has seen limited exploration and adds a novel perspective to the field of PK/PD modelling. Existing Natural Language Processing (NLP) pipelines, developed at UCL, will be used to collate PK/PD parameter prior distributions and ML guided PK/PD prediction algorithms developed in house will be further advanced to enable accommodation of prior distributions.

The successful candidate will use the first 12 months, i.e. MPhil phase, to conduct a detailed review of the literature to build hypotheses and to define research questions and corresponding objectives. This time should also be used for feasibility testing through data collection at UCL and GSK using readily available NLP pipelines and subsequent preliminary analyses.

After a successful upgrade to PhD student status the successful candidate will use the remaining three years to develop ML algorithms that incorporate prior distributions for the development of PK/PD models on various types of time series data that arise across the drug development pipeline from pre-clinical stages to Phase III. Developed ML-based algorithms will be benchmarked against conventional methods PK/PD modelling methods.

Ferran Gonzalez Hernandez, Quang Nguyen, Victoria C. Smith, José Antonio Cordero, Maria Rosa Ballester, Màrius Duran, Albert Solé, Palang Chotsiri, Thanaporn Wattanakul, Gill Mundin, Watjana Lilaonitkul, Joseph F. Standing, Frank Kloprogge. Named Entity Recognition of Pharmacokinetic parameters in the scientific literature. BioRxiv 2024.02.12.580001; doi:  https://doi.org/10.1101/2024.02.12.580001

Zhonghui Huang, Joseph F Standing, Frank Kloprogge. Development and exploration of exhaustive, stepwise, and heuristic algorithms for automated population pharmacokinetic modelling. PAGE 31 (2023) Abstr 10704 [ www.page-meeting.org/?abstract=10704]

Gonzalez Hernandez F, Carter SJ, Iso-Sipilä J, Goldsmith P, Almousa AA, Gastine S, Lilaonitkul W, Kloprogge F, Standing JF. An automated approach to identify scientific publications reporting pharmacokinetic parameters. Wellcome Open Res. 2021 Apr 21;6:88. doi: 10.12688/wellcomeopenres.16718.1. PMID: 34381873; PMCID: PMC8343403.

Environment

The student will be registered with Frank Kloprogge as primary supervisor at the Institute for Global Health pharmacokinetics-pharmacodynamics group. The student will work with a wider group of PhD students and post-docs, led by secondary supervisor Joseph Standing, at UCL’s Great Ormond Street Institute for Child Health and with computer sciences specialists at UCL. The student will also spend blocks of three months at GSK Stevenage under an industrial supervision team led by Nuria Buil-Bruna for data collection and testing of development ML models on real world industrial regulatory data. GSK is a science-led global biopharma company that aims to unite science, technology, and talent to get ahead of disease together. GSK undertakes research and development in a broad range of innovative products in the primary areas of pharmaceuticals and vaccines. GSK is working to positively impact the health of 2.5 billion people by the end of 2030. For further information, please visit GSK’s website.

The student will learn about all aspects of nonlinear mixed effects modelling of PK/PD time-series data, and the application of ML and embedding of prior distributions within this domain.

This Studentship presents a unique opportunity to conduct supervised research within an academic and industrial environment, and be a part of the research community and an integral part of the exciting and thriving research team.

We are looking for a successful candidate with, or is expected to receive, an upper second-class Bachelor’s degree in mathematics/statistics/engineering/computer sciences or in pharmacy/(bio-)medical sciences (or an overseas qualification of an equivalent standard). Furthermore, the candidate should be familiar with analysis of time series data using mixed-effects models, this specific skill may also have been acquired with a Master’s degree or equivalent work experience.

What we offer

This studentship provides a starting stipend of £25,237 per annum and covers the cost of tuition fees based on the UK (Home) rate. Additional funding for travel/conference fees is provided at £1,000 per annum and for consumables at £5,500 per annum. 

Eligibility

Non-UK students can apply but if they are not eligible for UK/Home fees status, will have to personally fund the difference between the UK (Home) rate and the Overseas rate.

NB: You will be asked about your likely fee status at the interview so we would advise you to contact the UCL Graduate Admissions Office for advice, should you be unsure whether or not you meet the eligibility criteria for Home fee status. EU nationals should see this  Student fee status page for information about eligibility for Home fees. See also to the UKCISA website (England: HE fee status).

Diversity, equity and inclusion

University College London and GSK are passionate about recruiting the best talent regardless of their background. All assessments are made on merit alone. We have support systems to protect the physical and mental well-being of all our staff and students and will make every effort to accommodate your personal circumstances by adopting a flexible working/study pattern to enable you to progress in your career while managing your circumstances.

How to Apply

Enquiries regarding the post can be made to Frank Kloprogge ( [email protected] )

To apply, please send 1.) a current two-page CV, 2.) a one-sided A4 motivation letter and 3.) the contact details of two professional referees to Frank Kloprogge ( [email protected] ). Please use the following subject line: PhD application “Towards efficient drug development modelling with machine learning”.

Closing deadline for applications: 17:00 14 June 2024 (GMT summer time)

Interview date/s: End of June/early July.

Applications that are submitted without following the correct application process, or those exceeding the page limits for CV’s and motivation letters will not be considered. The successful applicant will subsequently be required to apply to and register on the Global Health research degree to take up the studentship.

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