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Statistics and Machine Learning (DPhil)

University of Oxford

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University of Oxford

Course options

2 options available

  • Qualification

    PhD/DPhil - Doctor of Philosophy

  • Location

    University of Oxford

  • Study mode

    Full time

  • Start date

    29-SEP-25

  • Duration

    4 years

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.

Assessment

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

Students living in Hong Kong
(International fees)

£ 33,370per year

Tuition fees shown are for indicative purposes and may vary. Please check with the institution for most up to date details.

University information

University of Oxford

University of Oxford

  • University League Table

    2nd

  • Campus address

    University of Oxford, University Offices, Wellington Square, Oxford, Oxfordshire, OX1 2JD, England

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Subject rankings

  • Subject ranking

    Computer Science

    2nd out of 117

    2nd out of 65

  • Entry standards

    / Max 227
    214 94%

    5th

  • Graduate prospects

    / Max 100
    98.0 98%

    2nd

    7

  • Student satisfaction

    / Max 4
    3.21 80%

    12th

    10

  • Entry standards

    / Max 234
    218 93%

    3rd

  • Graduate prospects

    / Max 100
    91.0 91%

    4th

    1

  • Student satisfaction

    / Max 4
    n/a

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