Cohort-based doctoral programme in statistics and machine learning


This programme is jointly hosted by the Department of Mathematics, Imperial College London, and the Department of Statistics, University of Oxford.

The Statistics and Machine Learning cohort-based doctoral programme is a four-year PhD/DPhil research programme (or longer if studying part-time). It will train the next generation of researchers in statistics and statistical 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. The programme will provide students 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.

Each student will undertake a significant, challenging and original research project, leading to the award of a PhD/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.

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

The students will then begin their main PhD/DPhil project at the beginning of the third term, which can be based on one of the two mini-projects. Where appropriate for the research, student projects will be run jointly with the programme'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.

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

* Modern Statistical Theory;
* Statistical Machine Learning;
* Causality; and
* Bayesian Methods and Computation

Students will also be required to take a number of optional courses throughout their four years, which could be made up of choices from the following list: Advanced Monte Carlo methods, Causality and Graphical models, Networks, Nonparametric Bayes, Modern Asymptotics, Optimisation, (Deep) learning Theory and Practice, Reinforcement learning and Multi-Armed Bandits, Applied statistics, Genetics/computational biology, Generative Models, and more.

The studentships are fully funded for the four years and come with a generous allowance for travel, equipment and research costs. The new cohort of the programme will start in October 2024 with applications being invited now. Around 16 studentships are available, covering maintenance at an enhanced rate (current minimum £18,622 per year) plus tuition fees.

Studentships are open to all nationalities, and we are particularly keen to receive applications from women, minority groups and members of other groups that are underrepresented in technology. Applicants in possession of other funding scholarships or industry funding are also welcome to apply - please provide details of your funding source on your application.

We would like to receive applications from individuals who hold (or expect to receive) a masters level degree (or equivalent) in mathematics, statistics, physics, computer science, engineering, or in a closely related subject.

Further details, including the application procedure, can be found at

For more specific guidance, please visit each institution's respective website.

Oxford here:

Imperial here:


Please direct any enquiries to the programme's admissions team at:

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