phd maths oxford

DPhil in Statistics

• Entry requirements
• Funding and Costs

College preference

• How to Apply

About the course

In the DPhil in Statistics, you will investigate a particular project in depth and write a thesis which makes a significant contribution to the field. You will acquire a wide range of research and transferable skills, as well as in-depth knowledge, understanding and expertise in your chosen field of research. You will become part of a vibrant community of researchers.

The Department of Statistics in the University of Oxford is a world leader in research in probability, bioinformatics, mathematical genetics and statistical methodology, including computational statistics, machine learning and data science. Oxford’s Mathematical Sciences submission came first in the UK on all criteria in the 2021 Research Excellence Framework (REF) and in 2016 the department moved to a newly-refurbished building in the centre of Oxford.  

Much of the department’s research is either explicitly interdisciplinary or draws its motivation from application areas, ranging from genetics, immunoinformatics, bioinformatics and cheminformatics, to finance and the social sciences. 

You will be expected to acquire transferable skills as part of your training, and to undertake broadening training outside your specialist area. Part of that broadening training is obtained through APTS, the Academy for PhD Training in Statistics; this is a joint venture with a group of leading university statistics departments which runs four weeks of appropriate courses a year. You will give a research presentation or prepare a research poster each year in the department. There may also be opportunities to undertake industrial internships as appropriate.

You will be expected to teach approximately 12 contact hours per year in undergraduate and graduate courses in the department. This is mentored teaching, beginning with simple marking, to reach a point where individual students are leading whole classes of 10 to 12 undergraduate students. You will be encouraged to participate in social events and to take part in public engagement. The department also offers career development events.

The department runs seminar series in statistics and probability. There is also 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. Graduate training is an important part of the department's research mission. As well as the graduate lectures previously mentioned, formal lecture courses are also available, for example from the MSc in Statistical Science, from the fourth-year undergraduate courses in mathematics and statistics, and from the Centres for Doctoral Training. The MPLS Graduate School offers an extensive range of courses for graduate research students throughout the academic year, including academic subjects and skills; research skills and techniques; ethics and intellectual property; transferable, professional and personal effectiveness skills; and communication, interpersonal and teaching skills. Departmental seminars and colloquia bring research students, together with academic and other research staff, to hear about on-going research, and provide an opportunity for networking and socialising.

The course is full-time and requires attendance in Oxford. Full-time students are subject to the University's Residence requirements.

Provision exists for students on some courses to undertake their research in a ‘well-founded laboratory’ outside of the University. This may require travel to and attendance at a site that is not located in Oxford. Where known, existing collaborations will be outlined on this page. Please read the course information carefully, including the additional information about course fees and costs. 

Resources to support your study

As a graduate student, you will have access to the University's wide range of world-class resources including libraries, museums, galleries, digital resources and IT services.

The Bodleian Libraries is the largest library system in the UK. It includes the main Bodleian Library and libraries across Oxford, including major research libraries and faculty, department and institute libraries. Together, the Libraries hold more than 13 million printed items, provide access to e-journals, and contain outstanding special collections including rare books and manuscripts, classical papyri, maps, music, art and printed ephemera.

The University's IT Services is available to all students to support with core university IT systems and tools, as well as many other services and facilities. IT Services also offers a range of IT learning courses for students, to support with learning and research.

You will normally be provided with a computer and desk space in a shared office in the department. There are also spaces for study and collaborative learning, including a large interaction and social area, and an Open Research Zone.

You will have access to the Department of Statistics’ computing facilities and support, the department’s library (in addition to the nearby Radcliffe Science Library and other university libraries, and the centrally-provided electronic resources) and other facilities appropriate to your research topic. The provision of other resources specific to your research project should be agreed with your supervisor as a part of the planning stages of the agreed project.

Supervision

The allocation of graduate supervision for this course is the responsibility of the Department of Statistics and it is not always possible to accommodate the preferences of incoming graduate students to work with a particular member of staff. Under exceptional circumstances, a supervisor may be found outside the Department of Statistics.

You will be assigned a named supervisor or supervisors, who will have overall responsibility for the direction of your work on behalf of the department. You will have the opportunity to interact with fellow students and other members of your research groups, and more widely across the department. You will have the opportunity to meet with your supervisor, or a member of the supervisory team. 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. Typically, most students meet their supervisor as often as once every two weeks averaged across the year.

Initially, you will be admitted as a Probationer Research Student (PRS).

There are formal assessments of progress on the research project with the Transfer of Status from PRS to DPhil status at around 12 to 15 months and Confirmation of Status at around 30 to 36 months. These assessments involve the submission of written work and oral examination by two assessors (other than your supervisor). Over the course of the DPhil you will be expected to undertake a total of 100 hours of broadening training outside your specialist area.

The final thesis is normally submitted for examination during the fourth year and is followed by the viva examination.

Graduate destinations

After research degrees, the majority of the department’s graduates move into research and academic careers. Others work, for example, in data analytics, in tech and biotech companies and in the financial sector.

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 if a pandemic, epidemic or local health emergency occurs. 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.

For further information please see our page on changes to courses and the provisions of the student contract regarding changes to courses.

Entry requirements for entry in 2025-26

Proven and potential academic excellence.

The requirements described below are specific to this course and apply only in the year of entry that is shown. You can use our interactive tool to help you  evaluate whether your application is likely to be competitive .

Please be aware that any studentships that are linked to this course may have different or additional requirements and you should read any studentship information carefully before applying. 

Degree-level qualifications

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 an appropriate subject. You will need a strong background in mathematics and/or statistics.

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

A previous master's degree (either an integrated master's degree or standalone) is preferred but is not required.

For applicants with a degree from the USA, the minimum overall GPA that is normally required to meet the undergraduate-level requirement is 3.6 out of 4.0. However, most successful applicants have a GPA of 3.7.

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 can 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.

*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.

Supporting documents

You will be required to supply supporting documents with your application. 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 normally held as part of the admissions process for applicants who, on the basis of the written application, best meet the selection criteria. Interviews may be held in person, by telephone, or by video link such as Microsoft Teams or Zoom, normally with at least two interviewers.

The interviews last about 30 minutes and include questions about motivation as well as questions from the proposed research area.

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:

Financial Declaration

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.

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.

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. 

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.

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.

The department is located on St Giles, in a building providing excellent teaching facilities and creating a highly visible centre for statistics in 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.

Oxford’s Mathematical Sciences submission came first in the UK on all criteria in the 2021 Research Excellence Framework (REF).

View all courses   View taught courses View research courses

For entry in the 2025-26 academic year, the collegiate University expects to offer over 1,000 full or partial graduate scholarships across a wide range of graduate courses.

If you apply by the January deadline shown on this page and receive a course offer, your application will then be considered for Oxford scholarships. For the majority of Oxford scholarships, your application will automatically be assessed against the eligibility criteria, without needing to make a separate application. There are further Oxford scholarships available which have additional eligibility criteria and where you are required to submit a separate application. Most scholarships are awarded on the basis of academic merit and/or potential.

To ensure that you are considered for Oxford scholarships that require a separate application, for which you may be eligible,  use our fees, funding and scholarship search tool  to identify these opportunities and find out how to apply. Alongside Oxford scholarships, you should also consider other opportunities for which you may be eligible including  a range of external funding ,  loan schemes for postgraduate study  and any other scholarships which may also still be available after the January deadline as listed on  our fees, funding and scholarship search tool .

Details of college-specific funding opportunities can also be found on individual college websites:

Select from the list:

Please refer to the College preference section of this page to identify which of the colleges listed above accept students for this course.

For the majority of college scholarships, it doesn’t matter which college, if any, you state a preference for in your application. If another college is able to offer you a scholarship, your application can be moved to that college if you accept the scholarship. Some college scholarships may require you to state a preference for that college when you apply, so check the eligibility requirements carefully.

Annual fees for entry in 2025-26

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.

Continuation 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.

Where can I find further information about fees?

The Fees and Funding  section of this website provides further information about course fees , including information about fee status and eligibility  and your length of fee liability .

Additional information

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 and any additional course-specific costs, you will need to ensure that you have adequate funds to support your living costs for the duration of your course.

Living costs for full-time study

For the 2025-26 academic year, the range of likely living costs for a single, full-time student is between £1,425 and £2,035 for each month spent in Oxford. We provide the cost per month so you can multiply up by the number of months you expect to live in Oxford. Depending on your circumstances, you may also need to budget for the  costs of a student visa and immigration health surcharge and/or living costs for family members or other dependants that you plan to bring with you to Oxford (assuming that dependant visa eligibility criteria are met).

Further information about living costs

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. For study in Oxford beyond the 2025-26 academic year, it is suggested that you budget for potential increases in living expenses of around 4% each year – although this rate may vary depending on the national economic situation. For further information, please consult our more detailed information about living costs , which includes a breakdown of likely living costs in Oxford for items such as food, accommodation and study costs.

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 . 

If you are a current Oxford student and you would like to remain at your current Oxford college, you should check whether it is listed below. If it is, you should indicate this preference when you apply. If not, you should contact your college office to ask whether they would be willing to make an exception. Further information about staying at your current college can be found in our Application Guide. 

The following colleges accept students on the DPhil in Statistics:

• Balliol College
• Brasenose College
• Christ Church
• Corpus Christi College
• Exeter College
• Green Templeton College
• Hertford College
• Jesus College
• Keble College
• Kellogg College
• Lady Margaret Hall
• Linacre College
• Lincoln College
• Magdalen College
• Mansfield College
• Merton College
• New College
• Nuffield College
• Oriel College
• The Queen's College
• Reuben College
• St Anne's College
• St Catherine's College
• St Cross College
• St Edmund Hall
• St Hilda's College
• St Hugh's College
• St Peter's College
• Somerville College
• University College
• Wadham College
• Wolfson College
• Worcester College
• Wycliffe Hall

Before you apply

Our guide to getting started provides general advice on how to prepare for and start your application. You can use our interactive tool to help you evaluate whether your application is likely to be competitive .

If it is important for you to have your application considered under a particular deadline – eg under the 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 . Check the deadlines on this page and the information about deadlines and when to apply in our Application Guide.

Application fee waivers

An application fee of £20 is payable for each application to this course. 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 .

Application fee waivers for eligible associated courses

If you apply to this course and are considering applying (or have already applied) to any of the associated courses listed below , you can request an application fee waiver so that you only need to pay one application fee. We recommend that you use your application fee waiver to apply only for eligible courses that are closely related in research area to this one.

For full details about how to request an application fee waiver, please select the course you are interested in from the list below and refer to the equivalent section of its course page.

The following associated courses are taking part in this application fee waiver scheme:

• Autonomous Intelligent Machines and Systems , EPSRC CDT
• Biomedical and Clinical Sciences (Oxford-GSK), DPhil (expected to open soon)
• Cancer Science (Biological background) , CDT
• Cancer Science (Clinician) , DPhil
• Cancer Science (Intercalation) , DPhil
• Cancer Science (Maths/Physics background) , CDT
• Chemical Synthesis for a Healthy Planet , CDT
• Engineering Biology , BBSRC and EPSRC CDT
• Fusion Power , EPSRC CDT
• Genomic Medicine and Statistics , DPhil
• Healthcare Data Science , EPSRC CDT
• Inflammatory and Musculoskeletal Disease , DPhil
• Inorganic Materials for Advanced Manufacturing , EPSRC CDT
• Intelligent Earth , UKRI CDT in AI for the Environment
• Materials 4.0 , EPSRC CDT
• Mathematics of Random Systems: Analysis, Modelling and Algorithms , CDT
• Neuroscience (1+3), DPhil
• Quantum Informatics, EPSRC CDT   (expected to open soon)
• Robotics and AI for Net Zero, EPSRC CDT   (expected to open soon)
• Statistics and Machine Learning , EPSRC CDT
• Superconductivity: Enabling Transformative Technologies, EPSRC CDT   (expected to open soon)

Do I need to contact anyone before I apply?

You are advised to look at the research interests of the  department's academic staff  at an early stage and make contact with a potential supervisor via email to clarify your proposed research area. 

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 . 

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.

Proposed field and title of research project

Under the 'Field and title of research project' please enter your proposed field or area of research if this is known. If the department has advertised a specific research project that you would like to be considered for, please enter the project title here instead.

You should not use this field to type out a full research proposal. You will be able to upload your research supporting materials separately if they are required (as described below).

Proposed supervisor

If known, under 'Proposed supervisor name' enter the name of the academic(s) whom you would like to supervise your research. Otherwise, leave this field blank.

If possible, you should suggest one or two potential supervisors, listing them in order of preference or indicating equal preference.

Referees: 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.

Academic references are strongly encouraged, though a professional reference is acceptable in the exceptional case that the referee is able to offer comparable information on your background and suitability for the course to an academic referee.

Your references will support intellectual ability, academic achievement, motivation and commitment.

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 this course. Most applicants choose to submit a document of one to two pages highlighting their academic achievements and any relevant professional experience.

Research proposal: A maximum of 1,000 words

Your research proposal should be written in English and should specify the area in which your research interests lie and why you have chosen this area. If you have a particular project in mind, you should describe this and why you are keen to work on this.

If you do not have a detailed project in mind at this stage, you should describe your research interests instead. In this case, the description can be very brief but should include your reasons for applying.

The proposal should aim to be helpful to the department in the selection process and can include a suggestion for potential supervisor(s) and/or research group. The overall page count does not need to include any bibliography.

If possible, please ensure that the word count is clearly displayed on the document.

This will be assessed for:

• your reasons for applying
• evidence of motivation for and understanding of the proposed area of study.

Your statement should focus on specific research areas rather than personal achievements and aspirations.

Start or continue your application

You can start or return to an application using the relevant link below. As you complete the form, please  refer to the requirements above  and  consult our Application Guide for advice .

Apply Continue application

After you've submitted your application

Your application (including the supporting documents outlined above) will be assessed against the entry requirements detailed on this course page. Whether or not you have secured funding will  not  be taken into consideration when your application is assessed. You can  find out more about our shortlisting and selection process  in our detailed guide to what happens next.

Find out how to manage your application after submission , using our Applicant Self-Service tool.

ADMISSION STATUS

Open to applications for entry in 2025-26

12:00 midday UK time on:

Wednesday 8 January 2025

Latest deadline for most Oxford scholarships

Tuesday 4 March 2025

Applications may remain open after this deadline if places are still available - see below

A later deadline under 'Admission status'

If places are still available,  applications may be accepted after 4 March . The Admission status (above) will provide notice of any later deadline.

*Three-year average (applications for entry in 2022-23 to 2024-25)

Further information and enquiries

This course is offered by the Department of Statistics

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Course-related enquiries

Advice about contacting the department can be found in the How to apply section of this page

✉ [email protected] ☎ +44 (0)1865 272870

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DPhil in Statistics

The Department of Statistics admits doctoral students each year to a programme of instruction and research leading to the Doctor of Philosophy (DPhil) in Statistics degree. A doctorate normally requires between three and four years of full-time study. 

In the DPhil in Statistics, you will investigate a particular project in depth and write a thesis which makes a significant contribution to the field.   It can be in any of the subject areas for which supervision is available.

The Department of Statistics in the University of Oxford is a world leader in research in probability, bioinformatics, mathematical genetics and statistical methodology, including computational statistics and machine learning. Much of the department’s research is either explicitly interdisciplinary or draws its motivation from application areas, ranging from biology and physics to the social sciences.

You will be assigned a named supervisor or supervisors, who will have overall responsibility for the direction of your work on behalf of the department. You will have the opportunity to interact with fellow students and other members of your research groups, and more widely across the department. 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.

There are formal assessments of progress on the research project at around 12 to 15 months and at around 30 to 36 months. These assessments involve the submission of written work and oral examination.

The final thesis is normally submitted for examination during the fourth year and is followed by the viva examination.

You will be expected to acquire transferable skills as part of your training, and to undertake a total of 100 hours broadening training outside your specialist area. Part of that broadening training is obtained through APTS, the Academy for PhD Training in Statistics; this is a joint venture with a group of leading university statistics departments which runs four weeks of appropriate courses a year. You will give a research presentation or prepare a research poster each year in the department.

Our research students are actively involved in a lively academic community by means of seminars, lectures, journal clubs, working groups and social events. They receive training in modern probability, stochastic processes, statistical methodology, computational methods and transferable skills, in addition to specialised topics relevant to specific application areas. In particular, a broad structured programme of training in modern statistical methodology is available via courses in the Academy for Postgraduate Training in Statistics (APTS), of which the Department is a founding member.

Information about application deadlines, entry requirements and funding are available from the DPhil in Statistics prospectus page on the University of Oxford website.

Research Areas

Supervisors for DPhil projects are listed below, with links to the research group page.

Computational Statistics and Machine Learning

Supervisor: Professor François Caron Bayesian Statistics, Statistical Machine Learning, Statistical Network Analysis, Bayesian Nonparametrics.

Supervisor: Professor George Deligiannidis Computational statistics, in particular theory and methodology for Monte Carlo methods, especially MCMC and SMC for high-dimensional targets; limit theorems and convergence rates for Markov chains and stochastic processes in general; random walks.

Supervisor: Professor Arnaud Doucet Possible research areas: Bayesian Computation, Monte Carlo methods, Statistical Machine Learning.

Supervisor: Professor Tom Rainsforth Bayesian experimental design; Probabilistic and data-efficient approaches to machine learning; Active learning; Deep learning, with a particular focus on probabilistic approaches, deep representation learning, and deep generative models; Probabilistic programming; Approximate inference and Monte Carlo methods.

Supervisor: Professor Patrick Rebeschini Investigation of fundamental principles in high-dimensional probability, statistics and optimisation to design computationally efficient and statistically optimal algorithms for machine learning.

Supervisor: Professor Yee Whye Teh Machine learning. Probabilistic modelling, learning and inference.

Econometrics and Population Statistics

Supervisor: Professor Frank Windmeijer Causal Inference, Instrumental Variables Estimation (instrument selection using machine learning, weak instrument robust inference, bootstrap), Mendelian Randomisation.

Probability

Supervisor: Professor Julien Berestycki Branching processes, branching random walks, coalescence, fragmentation, population genetics, reaction-diffusion equations, front propagation, random trees.

Supervisor: Professor Alison Etheridge Stochastic analysis, especially problems related to stochastic modelling in population genetics.

Supervisor: Professor Christina Goldschmidt Research area: random discrete structures (eg trees and graphs) and their scaling limits.

Supervisor: Professor James Martin Probability theory, with strong links to statistical physics and theoretical computer science. Particular interests include: random graphs; interacting particle systems; models of random growth and percolation; models of coagulation and fragmentation; queueing networks.

Supervisor: Professor Gesine Reinert Investigation of networks such as protein-protein interaction networks and social networks in a statistically rigorous fashion. Often this will require some approximation, and approximations in statistics are another of my research interests. There is an excellent method to derive distances between the distributions of random quantities, namely Stein’s method, and I am interested in Stein’s method also from a theoretical viewpoint. The general area of my research falls under the category Applied Probability and many of the problems and examples I study are from the area of Computational Biology.

Supervisor: Professor David Steinsaltz Random dynamical systems, particularly with applications to population ecology. Evolutionary and biodemographic models of ageing.

Supervisor: Professor Matthias Winkel Probability and stochastic processes, in particular problems involving branching processes, Levy processes, fragmentation processes, random tree structures.

Protein Informatics

Supervisor: Professor Charlotte Deane Developing novel methodologies to understand and predict protein evolution, interaction, structure and function.

Supervisor: Professor Garrett Morris Developing novel therapeutics and improving our understanding of living systems at the molecular level, in particular methods development in computer-aided drug discovery. Harnessing the increasing amounts of experimental data, and the development of novel algorithms in chemoinformatics and bioinformatics, machine learning, network pharmacology, and structural biology, to help solve real-world drug discovery problems.

Oxford Protein Informatics Group

Statistical Genetics and Epidemiology

Supervisor: Professor Christl Donnelly Epidemiology of infectious disease; Real-time analysis of outbreaks; Biostatistics; Disease ecology; Applied statistics.

Supervisor: Professor Jotun Hein Algorithms in Bioinformatics, Computational Biology, Stochastic Models of Genealogies and Sequence Evolution, Mathematical Models of the Origin of Life, Stochastic Models of Network Evolution, Genome Analysis

Supervisor: Professor Simon Myers Statistical and population genomics (fine-scale population structure and migrations, recombination, natural selection on complex traits, association testing, demographic history), statistical approaches for single-cell data (RNA-seq, ATAC-seq), genetic determinants of speciation and fertility in mammals.

Supervisor: Professor Pier Palamara Computational methods for population genetics (natural selection, demographic history); statistical genetics (complex trait heritability, association); scalable methods for large genomic data sets.

Statistical Theory and Methodology

Supervisor:  Professor Robin Evans Graphical models; Causal inference; Marginal modelling; Combining causal information from different experimental settings; Confounding and selection bias; High-dimensional model selection, and low dimensional model selection in the presence of high-dimensional confounders.

Supervisor:  Professor Geoff Nicholls Applied Bayesian Statistics and Statistical Methods, focusing on building and fitting models for complex stochastic systems. Computational Statistics, in particular Monte Carlo Algorithms. Current projects: Multiple imputation and model misspecification; Monte Carlo filtering and inference for partial orders from rank data; Spatial Statistics and the location of texts; Phylogenetic inference for cultural traits.

Computational Biology and Bioinformatics

Queries about the DPhil in Statistics or MSc by Research in Statistics should be sent to [email protected]

Discover More

Our research.

Read about the research carried out at the Department, and find out more about our research groups.

Research Degrees FAQ

Find the answers to the most common questions about our research degrees.

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The Mathematical Institute offers a range of postgraduate taught and research programmes. As a large Mathematics department, the research done here offers extensive opportunities for learning and collaboration, alongside comprehensive student support and social networking. 

Key contacts

DPhil: @email

CDT: @email

MSc: Please see individual course pages

Our Courses

The Mathematical Institute offers five postgraduate taught (MSc) courses alongside postgraduate research (DPhil) opportunities. Three of our MSc courses are delivered in collaboration with other Departments: Computer Science, Physics, and Statistics. Our courses benefit from the strength of the Department's research and cover a wide range of areas.

MSc Courses

Centre for Doctoral Training (CDT)

Doctor of Philosophy (DPhil)

Scholarships.

There are scholarship opportunities available across our postgraduate courses. 

Scholarship News

Find out more.

Why Oxford?

International Students

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Course type

Qualification, university name, phd mathematics in oxford.

5 degrees at 1 university in Oxford.

Customise your search

Select the start date, qualification, and how you want to study

Related subjects:

• PhD Mathematics
• PhD General Mathematics
• PhD Statistics
• PhD Statistics, Probability and Operational Research

• Course title (A-Z)
• Course title (Z-A)
• Price: high - low
• Price: low - high

DPhil in Genomic Medicine and Statistics

University of oxford.

This four-year DPhil programme aims to train and nurture future leaders in the application of genomics to advance human health, equipping Read more...

• 4 years Full time degree: £10,070 per year (UK)

Mathematics of Random Systems: Analysis, Modelling and Algorithms (EPSRC Centre for Doctoral Training)

The Centre for Doctoral Training (CDT) in Mathematics of Random Systems is a four-year doctoral programme that offers academically Read more...

DPhil in Mathematics

The DPhil in Mathematics is an advanced research degree which provides the opportunity to investigate a project in depth and write a Read more...

• 3 years Full time degree: £10,070 per year (UK)

DPhil in Statistics

In the DPhil in Statistics, you will investigate a particular project in depth and write a thesis which makes a significant contribution Read more...

Statistics and Machine Learning (DPhil)

The Modern Statistics and Statistical Machine Learning CDT is a four-year DPhil research programme (or eight years if studying Read more...

• 8 years Part time degree: £5,035 per year (UK)

Course type:

• Full time PhD
• Part time PhD

Qualification:

Related subjects:.