menu icon
Book your open day visit nowClick to book open day
Modern Statistics and Statistical Machine Learning (EPSRC Centre for Doctoral Training)

Modern Statistics and Statistical Machine Learning (EPSRC Centre for Doctoral Training)

Different course options

Full time | University of Oxford | 4 years | 25-SEP-23

Study mode

Full time


4 years

Start date


Key information

Qualification type

PhD/DPhil - Doctor of Philosophy

Subject areas

Artificial Intelligence (Ai) Statistics

Course type


Course Summary

About the course

The Modern Statistics and Statistical Machine Learning 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 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.

This is the Oxford component of StatML, an EPSRC Centre for Doctoral Training (CDT) in Modern Statistics and Statistical Machine Learning, co-hosted by Imperial College London and the University of Oxford. The CDT 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 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.


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

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 full-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.

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’.

Tuition fees

UK fees
Course fees for UK students

For this course (per year)


International fees
Course fees for EU and international students

For this course (per year)


Entry requirements

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