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4-year funded doctoral studentships at Imperial and Oxford University

Imperial College London; Oxford University, UK
Full-time
On-site
London OR Oxford, UK
£21,237 GBP yearly

OPEN FOR ADMISSION – EPSRC Centre for Doctoral Training in Statistics and Machine Learning (StatML CDT) at Imperial and Oxford University (UK)


The Statistics and Machine Learning CDT (StatML)  offers a four-year (or longer if studying part-time) cohort-based doctoral (PhD/DPhil) programme in statistics and machine learning, jointly hosted by the Department of Mathematics, Imperial College London, and the Department of Statistics, University of Oxford. We are happy to announce we are now accepting applications for fully funded studentships starting in October 2025.  


The Statistics and Machine Learning CDT (StatML) is jointly hosted by the Department of Mathematics, Imperial College London, and the Department of Statistics, University of Oxford. 


StatML is a four-year (or longer if studying part-time) cohort-based doctoral (PhD/DPhil) programme that trains the next generation of researchers in statistics and machine learning. Student’s research focuses on developing widely applicable novel methodology and theory and creating application-specific methods, leading to breakthroughs in real-world problems in government, medicine, industry and science. The programme provides 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 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 an academic placement with some of the strongest statistics groups in the USA, Europe and Asia. 


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 2025 with applications being invited now. Around 16 studentships are available, covering maintenance at an enhanced rate (current minimum £21,237 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. 

Further details, including the application procedure and deadlines can be found at https://statml.io/index.php/how-to-apply/

Please direct any enquiries to the programme's admissions team at: frederique.godin@stats.ox.ac.uk or statml.io.admissions@imperial.ac.uk