About the course
The Statistics and 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 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 Statistics and Machine Learning, co-hosted by Imperial College London and the University of Oxford. The CDT will provide you 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.
You 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.
You will pursue two mini-projects during your first year (specific timings may vary for part-time students), with the expectation that one of them will lead to your main research project. At the admissions stage you will choose a mini-project. These mini-projects are proposed by the department's supervisory pool and industrial partners. You will be based at the home institution of your main supervisor of the first mini-project.
If your 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.
You will then begin your main 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, your project 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 your research projects you will engage with taught courses each lasting for two weeks. Core topics will be taught during at the beginning of your first year (specific timings may vary for part-time students) and are:
- Modern Statistical Theory
- Statistical Machine Learning;
- Causality; and
- Bayesian methods and computation.
You will also be required to take a number of optional courses throughout the four years of the course, which could be made up of choices from the following list: Bayesian nonparametrics; high-dimensional statistics; advanced optimisation; networks; reinforcement learning; large language models; conformal inference, variational Bayes and advance Bayesian computations, dynamical and graphical modelling of multivariate time series, modelling events; and deep learning. Optional modules last two weeks and are delivered in a similar format to the core modules.
Further information about part-time study
As a part-time student you will be required to attend modules and other cohort activities in Oxford (or sometimes London) for a minimum of 30 days each year. There will be no flexibility in the dates of modules or cohort events, though it is possible to spread your attendance at modules over the course of the four year programme (with agreement of your supervisor and the CDT Directors). Attendance will be required during term-time (on a pro-rata basis) for cohort activities. These often take place on Mondays and Thursdays. Attendance will occasionally be required outside of term-time for cohort activities.
You will have the opportunity to tailor your part-time study and skills training in liaison with your supervisor and CDT Directors, and agree your pattern of attendance.
The allocation of graduate supervision for this course is the responsibility of the Department of Statistics (Oxford) and/or the Department of Mathematics (Imperial). 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 these departments.
You are matched to your 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. It is normal for one of these mini-projects to lead to the full DPhil project with the same supervisory team as was in place for the mini-project chosen.
Typically, as a research student, you should expect to have meetings with your supervisor or a member of the supervisory team with a frequency of at least once every two weeks averaged across the year. The regularity of these meetings may be subject to variations according to the time of the year, and the stage that you are at in your research programme.
Each mini-project will be assessed on the basis of a report written by the student, by researchers from Imperial and Oxford.
Modules are assessed by a presentation in small groups on some material studied during the two-week module (known as micro-projects within the CDT).
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 part-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.
Full-time students will be expected to submit a thesis at four years from the date of admission. If you are studying part-time, you be required to submit your thesis after six or, at most, eight years from the date of admission. To be successfully awarded a DPhil in Statistics you will need to defend your thesis orally (viva voce) in front of two appointed examiners.
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. The final award for Oxford based students will be a DPhil awarded by the University of Oxford.
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’.
Changes to this course and your supervision
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. The safety of students, staff and visitors is paramount and major changes to delivery or services may have to be made in circumstances of a pandemic, epidemic or local health emergency. In addition, in certain circumstances, for example due to visa difficulties or because the health needs of students cannot be met, it may be necessary to make adjustments to course requirements for international study.
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 illness, sabbatical leave, parental leave or change in employment.
Entry requirements for entry in 2024-25
Proven and potential academic excellence
As a minimum, applicants should hold or be predicted to achieve the following UK qualifications or their equivalent:
- 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.
If your degree is not from the UK or another country specified above, visit our International Qualifications page for guidance on the qualifications and grades that would usually be considered to meet the University’s minimum entry requirements.
GRE General Test scores
No Graduate Record Examination (GRE) or GMAT scores are sought.
Other qualifications, evidence of excellence and relevant experience
Publications are not expected but details of any publications may be included with the application.
English language proficiency
This course requires proficiency in English at the University's standard level. If your first language is not English, you may need to provide evidence that you meet this requirement. The minimum scores required to meet the University's standard level are detailed in the table below.
|Test||Minimum overall score||Minimum score per component|
|IELTS Academic (Institution code: 0713)||7.0||6.5|
TOEFL iBT, including the 'Home Edition'
(Institution code: 0490)
*Previously known as the Cambridge Certificate of Advanced English or Cambridge English: Advanced (CAE)
†Previously known as the Cambridge Certificate of Proficiency in English or Cambridge English: Proficiency (CPE)
Your test must have been taken no more than two years before the start date of your course. Our Application Guide provides further information about the English language test requirement.
Declaring extenuating circumstances
If your ability to meet the entry requirements has been affected by the COVID-19 pandemic (eg you were awarded an unclassified/ungraded degree) or any other exceptional personal circumstance (eg other illness or bereavement), please refer to the guidance on extenuating circumstances in the Application Guide for information about how to declare this so that your application can be considered appropriately.
You will need to register three referees who can give an informed view of your academic ability and suitability for the course. The How to apply section of this page provides details of the types of reference that are required in support of your application for this course and how these will be assessed.
You will be required to supply supporting documents with your application, including an official transcript and a CV/résumé. The How to apply section of this page provides details of the supporting documents that are required as part of your application for this course and how these will be assessed.
Performance at interview
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 video link such as Zoom, 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.
How your application is assessed
Your application will be assessed purely on your proven and potential academic excellence and other entry requirements published under that heading.
References and supporting documents submitted as part of your application, and your performance at interview (if interviews are held) will be considered as part of the assessment process. Whether or not you have secured funding will not be taken into consideration when your application is assessed.
An overview of the shortlisting and selection process is provided below. Our 'After you apply' pages provide more information about how applications are assessed.
Shortlisting and selection
Students are considered for shortlisting and selected for admission without regard to age, disability, gender reassignment, marital or civil partnership status, pregnancy and maternity, race (including colour, nationality and ethnic or national origins), religion or belief (including lack of belief), sex, sexual orientation, as well as other relevant circumstances including parental or caring responsibilities or social background. However, please note the following:
- socio-economic information may be taken into account in the selection of applicants and award of scholarships for courses that are part of the University’s pilot selection procedure and for scholarships aimed at under-represented groups;
- country of ordinary residence may be taken into account in the awarding of certain scholarships; and
- protected characteristics may be taken into account during shortlisting for interview or the award of scholarships where the University has approved a positive action case under the Equality Act 2010.
Processing your data for shortlisting and selection
Admissions panels and assessors
All recommendations to admit a student involve the judgement of at least two members of the academic staff with relevant experience and expertise, and must also be approved by the Director of Graduate Studies or Admissions Committee (or equivalent within the department).
Admissions panels or committees will always include at least one member of academic staff who has undertaken appropriate training.
Other factors governing whether places can be offered
The following factors will also govern whether candidates can be offered places:
- the ability of the University to provide the appropriate supervision for your studies, as outlined under the 'Supervision' heading in the About section of this page;
- the ability of the University to provide appropriate support for your studies (eg through the provision of facilities, resources, teaching and/or research opportunities); and
- minimum and maximum limits to the numbers of students who may be admitted to the University's taught and research programmes.
Offer conditions for successful applications
If you receive an offer of a place at Oxford, your offer will outline any conditions that you need to satisfy and any actions you need to take, together with any associated deadlines. These may include academic conditions, such as achieving a specific final grade in your current degree course. These conditions will usually depend on your individual academic circumstances and may vary between applicants. Our 'After you apply' pages provide more information about offers and conditions.
In addition to any academic conditions which are set, you will also be required to meet the following requirements:
If you are offered a place, you will be required to complete a Financial Declaration in order to meet your financial condition of admission.
Disclosure of criminal convictions
In accordance with the University’s obligations towards students and staff, we will ask you to declare any relevant, unspent criminal convictions before you can take up a place at Oxford.
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 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.
The University's Department of Statistics is a world leader in research in probability, bioinformatics, mathematical genetics and statistical methodology, including computational statistics, machine learning and data science.
The department offers an MSc by Research in Statistics, a DPhil in Statistics and a DPhil programme in statistical science, the latter delivered by the department's EPSRC Centre for Doctoral Training in Statistical Science in Statistics and Machine Learning (known as StatML CDT). The department also admits graduates from the Mathematics of Random Systems CDT, Health Data Science CDT, and the Sustainable Approaches to Biomedical Science CDT, as well as other CDTs and Doctoral Training Programmes (DTPs).
As a research student you will be actively involved in a vibrant academic community by means of seminars, lectures, journal clubs, and social events. Research students are offered training in modern probability, stochastic processes, statistical methodology, computational methods and transferable skills, in addition to specialised topics relevant to specific application areas.
The department also offers a taught twelve-month MSc in Statistical Science, with a particular focus on modern computationally-intensive methods and their use in data analysis, which includes a dissertation component.
Much of the research in the Department of Statistics is either explicitly interdisciplinary or draws motivation from application areas, ranging from genetics, immunoinformatics, bioinformatics and cheminformatics, to finance and the social sciences.
In 2016 the department moved to a newly-renovated building in St Giles, providing excellent teaching facilities and creating a highly visible centre for statistics in Oxford. Oxford’s Mathematical Sciences submission came first in the UK on all criteria in the 2021 Research Excellence Framework (REF).
The University expects to be able to offer over 1,000 full or partial graduate scholarships across the collegiate University in 2024-25. You will be automatically considered for the majority of Oxford scholarships, if you fulfil the eligibility criteria and submit your graduate application by the relevant December or January deadline. Most scholarships are awarded on the basis of academic merit and/or potential.
For further details about searching for funding as a graduate student visit our dedicated Funding pages, which contain information about how to apply for Oxford scholarships requiring an additional application, details of external funding, loan schemes and other funding sources.
Please ensure that you visit individual college websites for details of any college-specific funding opportunities using the links provided on our college pages or below:
Please note that not all the colleges listed above may accept students on this course. For details of those which do, please refer to the College preference section of this page.
Further information about funding opportunities for this course can be found on the department's website.
Annual fees for entry in 2024-25
This information is not currently available. It will be updated when the course opens to applications. Please check back regularly for updates.
This information is not currently available. It will be updated when the course opens to applications. Please check back regularly for updates.
Information about course fees
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 changes to fees and charges.
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.
This information is not currently available. It will be updated when the course opens to applications. Please check back regularly for updates.
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 2024-25 academic year, the range of likely living costs for full-time study is between c. £1,345 and £1,955 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. The current economic climate and high national rate of inflation make it very hard to estimate potential changes to the cost of living over the next few years. When planning your finances for any future years of study in Oxford beyond 2024-25, it is suggested that you allow for potential increases in living expenses of around 5% each year – although this rate may vary depending on the national economic situation. UK inflationary increases will be kept under review and this page updated.
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.
Students enrolled on this course will belong to both a department/faculty and a college. Please note that ‘college’ and ‘colleges’ refers to all 43 of the University’s colleges, including those designated as societies and permanent private halls (PPHs).
If you apply for a place on this course you will have the option to express a preference for one of the colleges listed below, or you can ask us to find a college for you. Before deciding, we suggest that you read our brief introduction to the college system at Oxford and our advice about expressing a college preference. For some courses, the department may have provided some additional advice below to help you decide.
The following colleges accept students for full-time study on this course:
Before you apply
Our guide to getting started provides general advice on how to prepare for and start your application. Check the deadlines on this page and the information about deadlines in our Application Guide. If it's important for you to have your application considered under a particular deadline – eg under a December or January deadline in order to be considered for Oxford scholarships – we recommend that you aim to complete and submit your application at least two weeks in advance.
Application fee waivers
An application fee of £75 is payable per course application. Application fee waivers are available for the following applicants who meet the eligibility criteria:
- applicants from low-income countries;
- refugees and displaced persons;
- UK applicants from low-income backgrounds; and
- applicants who applied for our Graduate Access Programmes in the past two years and met the eligibility criteria.
You are encouraged to check whether you're eligible for an application fee waiver before you apply.
Readmission for current Oxford graduate taught students
If you're currently studying for an Oxford graduate taught course and apply to this course with no break in your studies, you may be eligible to apply to this course as a readmission applicant. The application fee will be waived for an eligible application of this type. Check whether you're eligible to apply for readmission.
Do I need to contact anyone before I 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. Please ensure that you have researched the specialisms of the department and those of your potential supervisor(s) before making contact. More information can be found on the StatML website.
You can either contact the academic staff member directly or route your enquiry via the Admissions Administrator using the contact details provided on this page.
Completing your application
You should refer to the information below when completing the application form, paying attention to the specific requirements for the supporting documents.
For this course, the application form will include questions that collect information that would usually be included in a CV/résumé. You should not upload a separate document. If a separate CV/résumé is uploaded, it will be removed from your application.
If any document does not meet the specification, including the stipulated word count, your application may be considered incomplete and not assessed by the academic department. Expand each section to show further details.
You will also need to complete the declaration form once you have applied for this course.
Proposed field and title of research project
Under 'Proposed supervisor name' enter the name of the academic (s) who you would like to supervise your research.
Three overall, academic preferred
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.
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.
Statement of purpose/personal statement:
A maximum of 1,100 words
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:
- What are your machine learning/statistical interests?
- Why do you think the Statistics and Machine Learning CDT is the right choice for you?
If possible, please ensure that the word count is clearly displayed on the document.
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.
As the admissions process for StatML will be run in parallel with Imperial College London, we ask that you please complete the declaration form once you have applied to one or both of the institutions.