Modern Statistics and Statistical Machine Learning (EPSRC CDT) | University of Oxford
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Modern Statistics and Statistical Machine Learning (EPSRC CDT)

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 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.

During their first three months (six months for part-time students) at the CDT students will work on their first mini-project, and during months four to six (seven to twelve months for part-time students) of their DPhil they will work on a second 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. Each mini-project will be assessed on the basis of a report written by the student, by researchers from Imperial and Oxford.

The students will then begin their main DPhil project, which can be based on one of the two mini-projects. 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.

Where appropriate for the research, student projects 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.

Alongside their research projects students will engage with taught courses each lasting for two weeks. Core topics will be taught during their first year (specific timings may vary for part-time students) and are: Bayesian Modelling and Computation, Statistical Machine Learning and Modern Statistical Theory. 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:

  1. Advanced Monte Carlo methods (Doucet, Gandy)
  2. Causality and Graphical models (Cohen, Evans, Holmes)
  3. Networks (Heard, Caron, Reinert)
  4. Nonparametric Bayes (Caron, Filippi, Rousseau)
  5. Modern Asymptotics (Battey, Reinert, Rousseau)
  6. Optimisation (Adams, Cartis, Hauser, Martin, Rebeschini)
  7. (Deep) learning Theory and Practice (Flaxman, Kanade, Tanner, Teh)
  8. Reinforcement learning and Multi-Armed Bandits (Adams, Filippi, Rebeschini)
  9. Applied statistics (Donnelly, Nicholls, Holmes)
  10. Genetics/ computational biology (Hein, Myers, Palamara)

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’.

Related courses

Changes to the course

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. For further information, please see our page on changes to courses.

Entry requirements for entry in 2019-20

Within equal opportunities principles and legislation, applications will be assessed in the light of an applicant’s ability to meet the following entry requirements:

1. Academic ability

Proven and potential academic excellence

Applicants are normally expected to be predicted or have achieved a first-class or strong upper second-class undergraduate degree with honours (or equivalent international qualifications), as a minimum, in mathematics, statistics, physics, computer science, engineering or a closely related subject. 

For applicants with a degree from the USA, the minimum GPA sought is 3.6 out of 4.0. 

However, entrance is very competitive and most successful applicants have a first-class degree or the equivalent. 

If you hold non-UK qualifications and wish to check how your qualifications match these requirements, you can contact the National Recognition Information Centre for the United Kingdom (UK NARIC).

No Graduate Record Examination (GRE) or GMAT scores are sought.

Other appropriate indicators will include:

Supporting documents

You will be required to supply supporting documents with your application, including references and an official transcript. See 'How to apply' for instructions on the documents you will need and how these will be assessed.

Performance at interview(s)

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 Skype, normally with at least two interviewers. Interviews will include some technical questions on statistical topics relating to the StatML programme. These questions will be adapted as far as possible to the applicant's own background training in statistics or machine learning. 


Publications are not expected but details of any publications may be included with the application. 

2. English language requirement

Applicants whose first language is not English are usually required to provide evidence of proficiency in English at the standard level required by the University.

3. Availability of supervision, teaching, facilities and places

The following factors will govern whether candidates can be offered places:

  • The ability of the Department of Statistics (Oxford and or/Imperial) to provide the appropriate supervision, research opportunities, teaching and facilities for your chosen area of work. 
  • Maximum and minimum limits to the number of students who may be admitted to Oxford's research and taught programmes.

The provision of supervision, where required, is subject to the following points:

  • The allocation of graduate supervision is the responsibility of the Department of Statistics (Oxford and/or Imperial) and 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 the Department of Statistics (Oxford and/or Imperial). 

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 sabbatical leave, maternity leave or change in employment. 

4. Disability, health conditions and specific learning difficulties

Students are selected for admission without regard to gender, marital or civil partnership status, disability, race, nationality, ethnic origin, religion or belief, sexual orientation, age or social background.

Decisions on admission are based solely on the individual academic merits of each candidate and the application of the entry requirements appropriate to the course.

Further information on how these matters are supported during the admissions process is available in our guidance for applicants with disabilities.

5. Assessors

All recommendations to admit a student involve the judgment of at least two members of academic staff with relevant experience and expertise, and additionally must be approved by the Director of Graduate Studies or Admissions Committee (or equivalent departmental persons or bodies).

Admissions panels or committees will always include at least one member of academic staff who has undertaken appropriate training.

6. Other information

Whether you have yet secured funding is not taken into consideration in the decision to make an initial offer of a place, but please note that the initial offer of a place will not be confirmed until you have completed a Financial Declaration.

Students are matched to their 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. 


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 new 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.

Starting in the second year, you will teach approximately twelve contact hours per year in undergraduate and graduate courses in your host department. 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.

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.

Tea and coffee facilities are provided in the Department. There are also opportunities for sporting interaction such as football and cricket.

Funding and costs

There are over 1,000 full graduate scholarships available across the University, and these cover your course fees and provide a grant for living costs. If you apply by the relevant January deadline and fulfil the eligibility criteria you will be automatically considered. Over two thirds of Oxford scholarships require nothing more than the standard course application. Use the Fees, funding and scholarship search to find out which scholarships you are eligible for and if they require an additional application, full details of which are provided.


Annual fees for entry in 2019-20

Full-time study

Fee status

Annual Course fees

Home/EU (including Islands)£7,665

Part-time study

Fee status

Annual Course fees

Home/EU (including Islands)£3,835

The fees shown above are the annual course fees for this course, for entry in the stated academic year.

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. You may have seen separate figures in the past for tuition fees and college fees. We have now combined these into a single figure.

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 likely increases to fees and 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.

For more information about course fees and fee liability, please see the Fees section of this website. EU applicants should refer to our dedicated webpage for details of the implications of the UK’s plans to leave the European Union.

Additional information

Full-time study

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.

Part-time study

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.

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 2019-20 academic year, the range of likely living costs for full-time study is between c. £1,058 and £1,643 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. When planning your finances for any future years of study in Oxford beyond 2019-20, you should allow for an estimated increase in living expenses of 3% each year.

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.


The following colleges accept students for full-time study on the Modern Statistics and Statistical Machine Learning CDT:

The following colleges accept students for part-time study on the Modern Statistics and Statistical Machine Learning CDT:

How to 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. More information can be found on the StatML website.

To apply to the Modern Statistics and Statistical Machine Learning CDT, applicants should follow this process:

  • send an application to the CDT at Imperial, via (further details about the application process and what you should submit can be found on the StatML website);
  • the CDT will direct you to apply to Oxford or Imperial, depending on your interests;
  • assessment and interviews will take place at Imperial or Oxford once applications have been received; and
  • offers will be made by Imperial, or by Oxford if you are directed to apply there. 

If you are directed to apply to Oxford, you will be provided with a link to our online application form. The set of documents you will be required to submit via our online form comprises the following:

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.


A CV/résumé is compulsory for all applications. Most applicants choose to submit a document of one to two pages highlighting their academic achievements and any relevant professional experience.

Statement of purpose/personal statement:
One to two pages

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:

  1. What are your machine learning/statistical interests?
  2. Why do you think the Modern Statistics and Statistical Machine Learning CDT is the right choice for you?

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.

References/letters of recommendation:
Three overall, generally academic

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.

Application Guide

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