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phd statistics cambridge

Research in DPMMS is actively undertaken across a range of modern mathematics.

The pages for individual members of DPMMS give information about each person's research interests.

DPMMS also hosts:

  • The Cambridge Mathematics of Information in Healthcare Hub ( CMIH) 
  • Cambridge Centre for Analysis (Centre for Doctoral Training)
  • Cambridge Mathematics of Information (Centre for Doctoral Training)
  • Winton Centre for Risk and Evidence Communication

Postdoctoral Opportunities

  • Postdoc opportunities at DPMMS
  • Postdocs of Cambridge Society (PdOC)

Research Students

  • Information for new PhD students
  • Information for current research students

Special Lectures and Events

Regular Seminars

Annual Lectures

  • The  Mordell Lecture is given annually by an invited speaker for a research-level audience
  • The  Rouse Ball Lecture is sponsored jointly with DAMTP  but aimed at undergraduates.
  • The  Peter Whittle  Lecture is given annually by an invited speaker in the field of Statistics  
  • DPMMS Colloquia

Research conferences 

  • Archive of past events

Thesis Archive

DPMMS maintains a PhD thesis archive on Apollo

Guidance on adding new theses to the archive is available .

Related sites

  • Statistical Laboratory
  • Faculty of Mathematics
  • Isaac Newton Institute for Mathematical Sciences

© 2024 University of Cambridge

  • University A-Z
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Study at Cambridge

About the university, research at cambridge.

  • Events and open days
  • Fees and finance
  • Student blogs and videos
  • Why Cambridge
  • Qualifications directory
  • How to apply
  • Fees and funding
  • Frequently asked questions
  • International students
  • Continuing education
  • Executive and professional education
  • Courses in education
  • How the University and Colleges work
  • Visiting the University
  • Term dates and calendars
  • Video and audio
  • Find an expert
  • Publications
  • International Cambridge
  • Public engagement
  • Giving to Cambridge
  • For current students
  • For business
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Statistical Laboratory

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Rollo Davidson Trust

The Statistical Laboratory is located in Pavilion D of the Centre for Mathematical Sciences. It is sub-department of the Department of Pure Mathematics and Mathematical Statistics , which in turn is part of the Faculty of Mathematics . We have about 35 members , made up of permanent staff, post-docs, and post-graduate students. Our interests cover a broad range of statistics, probability and operational research.

Congratulations to Jason Miller

awarded the 2023 Fermat Prize

awarded an ERC consolidator grant

Congratulations to Po-Ling-Loh

awarded Philip Leverhulme Prize, 2023

Thomas Bond Sprague Prize, 2023

Congratulations to L.J. Hill and M. Augustynowicz both of Trinity College jointly awarded the 2023 Thomas  Bond Sprague Prize

Senior Academic Promotions

Congratulations to Kaisey Mandel on his promotion to Professor (Grade 11) and to Roland Bauerschmidt and Rajen Shah on their promotion to Professor (Grade 12)

Congratulations to Duncan Dauvergne (University of Toronto),  Nina Holden (New York University) and Xin Sun (University of Pennsylvania)   jointly awarded Rollo Davidson Prize, 2023

Congratulations to Yiannis Kontoyiannis

elected to Fellowship in the Institute of Mathematical Statistics

Congratulations to Richard Nickl

awarded an ERC Advanced Grant 

jointly awarded the 2023 Leonard Eisenbud Prize for works on random two-dimensional geometries, and in particular on Liouville Quantum Gravity.

Frontpage talks

Tweets by DPMMS

Related sites

  • Faculty of Mathematics

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Three students in a discussion

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 are 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 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. 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.

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 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.

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 2024-25

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 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 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.

How your application is assessed

Your application will be assessed purely on your proven and potential academic excellence and other entry requirements described 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

Information about  processing special category data for the purposes of positive action  and  using your data to assess your eligibility for funding , can be found in our Postgraduate Applicant Privacy Policy.

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:

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.

The Department of Statistics is based in St Giles, near the centre of Oxford. The building has spaces for study and collaborative learning, including a large interaction and social area, the Library and an Open Research Zone.

You will normally be provided with a computer and desk space in a shared office in this building.

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.

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. There are various social events held throughout the year, such as board game evenings, choir practice, a Summer party and a Winter party.

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

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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.

Annual fees for entry in 2024-25

Further details about fee status eligibility can be found on the fee status webpage.

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, 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.

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 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'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 . Check the deadlines on this page and the  information about deadlines  in our Application Guide.

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 .

Application fee waivers for eligible associated courses

If you apply to this course and up to two eligible associated courses from our predefined list during the same cycle, you can request an application fee waiver so that you only need to pay one application fee.

The list of eligible associated courses may be updated as new courses are opened. Please check the list regularly, especially if you are applying to a course that has recently opened to accept applications.

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 .

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.

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) who 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.

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 . You'll find the answers to most common queries in our FAQs.

Application Guide   Apply


Open - applications are still being accepted

Up to a week's notice of closure will be provided on this page - no other notification will be given

12:00 midday UK time on:

Friday 5 January 2024 Latest deadline for most Oxford scholarships

Friday 1 March 2024 Applications may remain open after this deadline if places are still available - see below

A later deadline shown under 'Admission status' If places are still available,  applications may be accepted after 1 March . The 'Admissions status' (above) will provide notice of any later deadline.

*Three-year average (applications for entry in 2021-22 to 2023-24)

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

Application-process enquiries

See the application guide

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Department of Statistics


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The department encourages research in both theoretical and applied statistics. Faculty members of the department have been leaders in research on a multitude of topics that include statistical inference, statistical computing and Monte-Carlo methods, analysis of missing data, causal inference, stochastic processes, multilevel models, experimental design, network models and the interface of statistics and the social, physical, and biological sciences. A unique feature of the department lies in the fact that apart from methodological research, all the faculty members are also heavily involved in applied research, developing novel methodology that can be applied to a wide array of fields like astrophysics, biology, chemistry, economics, engineering, public policy, sociology, education and many others.

Two carefully designed special courses offered to Ph.D. students form a unique feature of our program. Among these, Stat 303 equips students with the  basic skills necessary to teach statistics , as well as to be better overall statistics communicators. Stat 399 equips them with generic skills necessary for problem solving abilities.

Our Ph.D. students often receive substantial guidance from several faculty members, not just from their primary advisors, and in several settings. For example, every Ph.D. candidate who passes the qualifying exam gives a 30 minute presentation each semester (in Stat 300 ), in which the faculty ask questions and make comments. The Department recently introduced an award for Best Post-Qualifying Talk (up to two per semester), to further encourage and reward inspired research and presentations.


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  • The majority of current PhD studentships are listed on the  University's Jobs site
  • For a full list of departments and faculties at the University, visit this page where you can learn more about the research interests within each department
  • To find academics you might like to work with, use our directory

Graduate Admissions

The  Graduate Admissions  office provides a range of information on postgraduate programmes at Cambridge, along with a step-by-step guide to the application process. It is advisable to start researching funding opportunities at least a year before your course begins.

MPhil and PhD course relevant to data science - from across University of Cambridge

Please visit the relevant pages and contact the relevant education provider if you have queries. You should pay particular attention to the entry requirements and guidance for applicants there.

MPhil in Machine Learning and Machine Intelligence - an eleven month full-time programme offered by the Machine Learning Group, the Speech Group, and the Computer Vision and Robotics Group in the Cambridge University Department of Engineering.  The course aims to teach the state-of-the-art in machine learning, speech and language processing, and computer vision; to give students the skills and expertise necessary to take leading roles in industry and to equip them with the research skills necessary for doctoral study at Cambridge and other universities.

PhD programme in Advanced Machine Learning - The Machine Learning Group is based in the Department of Engineering, and encourages applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific applications. 

Cambridge Centre for AI in Medicine - Cambridge Centre for AI in Medicine (CCAIM) is a multi-disciplinary centre established by the University of Cambridge in 2020 to develop pioneering AI machine learning (ML) technologies that will transform biomedical science, medicine and healthcare. PhD studentships are oten available, please check their website for details.

SynTech Centre for Doctoral Training - EPSRC Centre for Doctoral Training in Next Generation Synthetic Chemistry Enabled by Digital Molecular Technologies. An interdisciplinary cohort-driven programme to produce the next generation of molecule making scientists by combining Synthetic Chemistry, Chemical Engineering, Engineering, Machine Learning and Artificial Intelligence.

Advanced Computer Science MPhil  - The MPhil in Advanced Computer Science (the ACS) is designed to prepare students for doctoral research, whether at Cambridge or elsewhere. Typical applicants will have undertaken a first degree in computer science or an equivalent subject, and will be expected to be familiar with basic concepts and practices. The ACS is a nine–month course which starts in early October and finishes on 30 June. It covers advanced material in both theoretical and practical areas as well as instilling the elements of research practice.

Application of Artificial Intelligence to the study of Environmental Risks MRes and PhD - The UKRI Centre for Doctoral Training in the Application of Artificial Intelligence to the study of Environmental Risks (AI4ER) trains researchers (through several multidisciplinary cohorts) to be uniquely equipped to develop and apply leading-edge computational approaches to address critical global environmental challenges by exploiting vast, diverse and often currently untapped environmental data sets. Embedded in the outstanding research environments of the University of Cambridge and the British Antarctic Survey (BAS), the AI4ER CDT addresses problems that are relevant to  building resilience to environmental hazards and managing environmental change .

Postgraduate Study in Mathematics - Various postgraduate courses of a mathematical nature are available at the University of Cambridge, including both taught courses and research degrees.

Mathematics of Information PhD  - This cutting-edge training Centre in the Mathematics of Information produces a new generation of leaders in the theory and practice of modern data science, with an emphasis on the mathematical underpinnings of this new scientific field. The Cambridge Mathematics of Information (CMI) PhD is a four-year course leading to a single PhD thesis.

Cambridge Computational Biology Institute MPhil and PhD ​ - The MPhil in Computational Biology course is aimed at introducing students in the biological, mathematical and physical sciences to quantitative aspects of modern biology and medicine, including bioinformatics. The course has been developed by the Cambridge Computational Biology Institute and is run by the Department of Applied Mathematics and Theoretical Physics at the Centre for Mathematical Sciences (CMS).

Centre for Scientific Computing MPhil and PhD  - The MPhil programme on Scientific Computing is offered by the University of Cambridge as a full-time course which aims to provide education of the highest quality at Master’s level. A common route for admission into our PhD programme is via the Centre’s MPhil programme in Scientific Computing.

Part III Mathematics  - Part III is a 9 month taught masters course in mathematics.  It is an excellent preparation for mathematical research and it is also a valuable course in mathematics and in its applications for those who want further training before taking posts in industry, teaching, or research establishments. Students admitted from outside Cambridge to Part III study towards the Master of Advanced Study (MASt).  Students continuing from the Cambridge Tripos for a fourth year, study towards the Master of Mathematics (MMath).  The requirements and course structure for Part III are the same for all students irrespective of whether they are studying for the MASt or MMath degree. There are over 200 Part III (MASt and MMath) students each year; almost all are in their fourth or fifth year of university studies. 

School of Clinical Medicine Graduate Training Office - Prospective students interested in pursuing a graduate degree course in a subject area related to clinical medicine at the University of Cambridge should consult the School’s individual departmental websites for detailed information about the courses which they run and the University’s Graduate Admissions website for information on the application process and on funding opportunities.

Centre for Doctoral Training in Data, Risk And Environmental Analytical Methods  - The CDT embraces a wide range of world-leading Doctoral research in the area of Big Data and Environmental Risk Mitigation. The CDT research underway seeks to utilise emerging technologies, techniques and tools, to more accurately monitor the environment, enabling cutting edge research. To provide end-users with more integrated information at improved temporal and spatial resolutions to deliver solutions to environmental challenges (both acute and long- term). Funded by  NERC  (the Natural Environment Research Council, NERC Ref: NE/M009009/1), the DREAM (Data, Risk and Environmental Analytical Methods) consortium is made up of Cranfield, Newcastle, Cambridge and Birmingham universities.

Centre for Doctoral Training in Data Intensive Science  - The Cambridge CDT in Data Intensive Science is an innovative, interdisciplinary centre, distributed between the Department of Physics (Cavendish Laboratory), Department of Applied Mathematics and Theoretical Physics (DAMTP), Department of Pure Mathematics and Mathematical Statistics (DPMMS) and the Institute of Astronomy (IoA).

MPhil in Data Intensive Science - This course aims to take science graduates and to prepare them for data intensive research careers by providing advanced training in three key areas – Statistical Analysis, Machine Learning, and Research Computing – and their application to current research frontiers.

Cambridge Digital Humanities - The MPhil provides the opportunity to specialise in a chosen subject area as well as an advanced level introduction to DH approaches, methods and theory. The course provides critical and practical literacy, the chance to advance an extant specialization by re-contextualizing it in relation to advanced theoretical work, and the chance to develop as a DH scholar.

The Cambridge Centre for Data-Driven Discovery (C2D3) brings together researchers and expertise from across the academic departments and industry to drive research into the analysis, understanding and use of data science and AI. C2D3 is an Interdisciplinary Research Centre at the University of Cambridge.

  • Supports and connects the growing data science and AI research community 
  • Builds research capacity in data science and AI to tackle complex issues 
  • Drives new research challenges through collaborative research projects 
  • Promotes and provides opportunities for knowledge transfer 
  • Identifies and provides training courses for students, academics, industry and the third sector 
  • Serves as a gateway for external organisations 

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PhD in Biostatistics

University of cambridge, different course options.

  • Key information

Course Summary

Tuition fees, entry requirements, similar courses at different universities, key information data source : idp connect, qualification type.

PhD/DPhil - Doctor of Philosophy

Subject areas

Statistics Biology Mathematics For Specific Applications

Course type

The MRC Biostatistics Unit is an internationally recognised research department of the University of Cambridge specialising in statistical modelling with application to medical, biological or public health sciences.

Our PhD students are registered with the University of Cambridge. Students belong to one of the University's Colleges and are trained at our Unit at the University Forvie Site on the Cambridge Biomedical Campus at Addenbrooke's Hospital.

We maintain strong links with the University of Cambridge Statistical Laboratory, Alan Turing Institute and other mathematical departments (who are based in the Centre for Mathematical Sciences on the West Cambridge site).

For the PhD degree, the thesis should not exceed 60,000 words (or 80,000 by special permission of the Degree Committee). This limit excludes figures, photographs, tables, appendices and bibliography. Formatting should be one-and-a-half spaced and pages should be double-sided.

Submission of the final thesis will be followed by an oral examination.

All PhD students are required to undergo formal assessment (by written report and viva) in the final quarter of their first year. If successful, the student moves from being "probationary" to being registered for the PhD and can proceed with their project.

Further informal assessment via presentation takes place in the first term of Year 3.

UK fees Course fees for UK students

For this course (per year)

International fees Course fees for EU and international students

Applicants for this course should have achieved a UK High II.i Honours Degree.

Mathematics, Operational Research, Statistics and Econometrics (MORSE) Masters/MSc

University of birmingham, msc statistics for computational biology, aberystwyth university, statistics - phd, university of kent, statistics - msc, statistical data science with an industrial placement - msc.

The University of Edinburgh home

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Postgraduate study

Statistics PhD

Awards: PhD

Study modes: Full-time, Part-time

Funding opportunities

Programme website: Statistics

Discovery Day

Join us online on 18th April to learn more about postgraduate study at Edinburgh

View sessions and register

Research profile

Our society revolves around variation, uncertainty and risk. By gaining a greater understanding of these variables through the study of statistics, we’re able to create systems and techniques that benefit areas as diverse as science, law and finance.

Our Statistics research group explores a wide range of statistical theory and practice, often applying its findings in collaboration with researchers in related fields, such as informatics, geosciences, medicine and biomathematics. The group leads the interdisciplinary Centre for Statistics that spans across the whole breadth of the university, providing opportunities for collaborations with researchers in many different applied fields.

The School of Mathematics is a vibrant community with researchers in many different, but related, fields - including Data Science.

Our research is balanced between classical and Bayesian statistics. Particular areas of interest include, but not limited to, high-dimensional data, computationally intensive techniques, wavelets, nonparametric regression, extreme value theory, sampling and hidden process models.

While the group has a strong theoretical base, a key component in the research relates to the interdisciplinary aspects of statistics with specific application areas including for example, ecology, geosciences, medicine, forensic science, law, and functional genomics data, such as gene expression microarrays.

Training and support

As a research student, you’ll find a wealth of expertise available to you via our links with theorists and practitioners in related fields.

The School interacts with numerous other groups across the university, including for example, Informatics, Geosciences, Business, Clinical Trials Unit. The interdisciplinary Centre for Statistics connects individuals across the breadth of the university interested in cross-fertilisation and collaborative research. The recently opened Bayes Centre, which also hosts the International Centre for Mathematical Sciences is the College of Science and Engineering Data Science initiative providing an exciting interdisciplinary environment for interacting within and across Schools.

In addition, the Scottish Government-backed research provider Biomathematics and Statistics Scotland is an associated research institute of the University. With its main base in our building, it provides access to other researchers with an interest in statistical genomics and bioinformatics, process and systems modelling and statistical methodology.

If your research is in the expanding area of forensic statistics, you'll benefit from our link with the Joseph Bell Centre for Forensic Statistics and Legal Reasoning. The Centre applies and teaches statistical techniques for interpreting evidence, such as binomial probabilities, conditional probability and Bayes’ Theorem.

Mathematics is a discipline of high intellect with connections stretching across all the scientific disciplines and beyond, and in Edinburgh you can be certain of thriving in a rich academic setting. Our School is one of the country’s largest mathematics research communities in its own right, but you will also benefit from Edinburgh’s high-level collaborations, both regional and international.

Research students will have a primary and secondary supervisor and the opportunity to network with a large and varied peer group. You will be carrying out your research in the company of eminent figures and be exposed to a steady stream of distinguished researchers from all over the world.

Our status as one of the most prestigious schools in the UK for mathematical sciences attracts highly respected staff. Many of our 70 current academics are leaders in their fields and have been recognised with international awards.

Researchers are encouraged to travel and participate in conferences and seminars. You will also be in the right place in Edinburgh to meet distinguished researchers from all over the world who are attracted to conferences held at the School and the various collaborative centres based here. You will find opportunities for networking that could have far-reaching effects on your career in statistics.

You will enjoy excellent facilities, ranging from one of the world’s major supercomputing hubs to generous library provision for research at the leading level, including the new Noreen and Kenneth Murray Library at King’s Buildings.

Students have access to more than 1,400 computers in suites distributed across the University’s sites, many of which are open 24 hours a day. In addition, if you are a research student, you will have your own desk with desktop computer.

We provide all our mathematics postgraduates with access to software packages such as Maple, Matlab and Mathematica. Research students are allocated parallel computing time on ‘Eddie’ – the Edinburgh Compute and Data Facility. It is also possible to arrange use of the BlueGene/Q supercomputer facility if your research requires it.

Career opportunities

You will gain a qualification that is highly regarded in both academia and industry. Future career options are diverse, with past students finding positions in academic institutions, forensics, finance, law and biological and agricultural organisations.

Statistics MSc Graduates 2017

Entry requirements.

These entry requirements are for the 2024/25 academic year and requirements for future academic years may differ. Entry requirements for the 2025/26 academic year will be published on 1 Oct 2024.

A UK first class honours degree, or its international equivalent, in an appropriate subject; or a UK 2:1 honours degree plus a UK masters degree, or their international equivalents; or relevant qualifications and experience.

International qualifications

Check whether your international qualifications meet our general entry requirements:

  • Entry requirements by country
  • English language requirements

Regardless of your nationality or country of residence, you must demonstrate a level of English language competency at a level that will enable you to succeed in your studies.

English language tests

We accept the following English language qualifications at the grades specified:

  • IELTS Academic: total 6.5 with at least 6.0 in each component.
  • TOEFL-iBT (including Home Edition): total 92 with at least 20 in each component. We do not accept TOEFL MyBest Score to meet our English language requirements.
  • C1 Advanced ( CAE ) / C2 Proficiency ( CPE ): total 176 with at least 169 in each component.
  • Trinity ISE : ISE II with distinctions in all four components.
  • PTE Academic: total 62 with at least 59 in each component.

Your English language qualification must be no more than three and a half years old from the start date of the programme you are applying to study, unless you are using IELTS , TOEFL, Trinity ISE or PTE , in which case it must be no more than two years old.

Degrees taught and assessed in English

We also accept an undergraduate or postgraduate degree that has been taught and assessed in English in a majority English speaking country, as defined by UK Visas and Immigration:

  • UKVI list of majority English speaking countries

We also accept a degree that has been taught and assessed in English from a university on our list of approved universities in non-majority English speaking countries (non-MESC).

  • Approved universities in non-MESC

If you are not a national of a majority English speaking country, then your degree must be no more than three and a half years old at the beginning of your programme of study.

Find out more about our language requirements:

  • Academic Technology Approval Scheme

If you are not an EU , EEA or Swiss national, you may need an Academic Technology Approval Scheme clearance certificate in order to study this programme.

Fees and costs

Tuition fees, scholarships and funding, featured funding.

  • School of Mathematics funding opportunities

UK government postgraduate loans

If you live in the UK, you may be able to apply for a postgraduate loan from one of the UK's governments.

The type and amount of financial support you are eligible for will depend on:

  • your programme
  • the duration of your studies
  • your tuition fee status

Programmes studied on a part-time intermittent basis are not eligible.

  • UK government and other external funding

Other funding opportunities

Search for scholarships and funding opportunities:

  • Search for funding

Further information

  • Graduate School Administrator
  • Phone: +44 (0)131 650 5085
  • Contact: [email protected]
  • School of Mathematics
  • James Clerk Maxwell Building
  • Peter Guthrie Tait Road
  • The King's Buildings Campus
  • Programme: Statistics
  • School: Mathematics
  • College: Science & Engineering

Select your programme and preferred start date to begin your application.

PhD Statistics - 3 Years (Full-time)

Phd statistics - 6 years (part-time), application deadlines.

We strongly recommend you submit your completed application as early as possible, particularly if you are also applying for funding or will require a visa. We may consider late applications if we have places available. All applications received by 22 January 2024 will receive full consideration for funding. Later applications will be considered until all positions are filled.

  • How to apply

You must submit two references with your application.

Find out more about the general application process for postgraduate programmes:

Report a problem

Thank you, your report has been submitted. We will deal with the issue as soon as possible. If you have any questions or would like to receive a follow-up, please send an email to [email protected] .

phd statistics cambridge

University of Cambridge

PhD Pure Mathematics and Mathematical Statistics

1 in 5 applicants to this programme received an offer.

Data shown above is for entry in academic year 2021/22 (sources) .

Previous Years

Data sources.

  • FOI Request by Albert Warren.
  • FOI Request by Ash Rizwan. January 2017.
  • FOI Request by Lai Yinsheung. August 2022.

The acceptance rate , or offer rate, represents the fraction of applicants who received an offer. Note that this will be generally lower the acceptances rates (acceptances divided by applicants) published by many other sources. This article explains it in more detail. The acceptances generally indicate the number of offer holders who accepted the offer and fulfilled its conditions. For some universities, however, it denotes the number of applicants who accepted the offer, regardless of whether they subsequently met its conditions.

Data Reliability

Unless otherwise noted, the data presented comes from the universities and is generally reliable. However, some of the differences between years and/or courses may be due to different counting methodologies or data gathering errors. This may especially be the case if there is a sharp difference from year to year. If the data does not look right, click the "Report" button located near the top of the page.

phd statistics cambridge

The Chinese University of Hong Kong

Department of statistics.

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phd statistics cambridge

Our former PhD student Dr. Shen Guohao (currently an Assistant Professor in The Hong Kong Polytechnic University) who was supervised by Prof. Lin Yuanyuan was awarded the HKSS – John Aitchison Prize in Statistics, 2024.

The Prize was established in 2022 by the Hong Kong Statistical Society (HKSS), to honour the distinguished career and contributions of Professor John Aitchison (1926-2016), Chair Professor of Statistics at the University of Hong Kong between 1976 and 1989 and founder and first President of the HKSS.

The objective of the Prize is to reward excellence in PhD research, as demonstrated by a research paper, in Statistics or a closely related discipline.

For more details, please visit:

phd statistics cambridge

Copyright © 2020. All Rights Reserved. The Chinese University of Hong Kong.

Copyright © 2024. All Rights Reserved. The Chinese University of Hong Kong.

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your money adviser

Report Helps Answer the Question: Is a College Degree Worth the Cost?

The analysis found that former students at most colleges had an annual income higher than high school graduates a decade after enrollment.

A diploma being swiped through a green device with a clock on it.

By Ann Carrns

Most people go to college to improve their financial prospects, though there are other benefits to attending a postsecondary institution. But as the average cost of a four-year degree has risen to six figures, even at public universities, it can be hard to know if the money is well spent .

A new analysis by HEA Group, a research and consulting firm focused on college access and success, may help answer the question for students and their families. The study compares the median earnings of former college students, 10 years after they enrolled, with basic income benchmarks.

The analysis found that a majority of colleges exceed minimum economic measures for their graduates, like having a typical annual income that is more than that of a high school graduate with no higher education ($32,000, per federal Scorecard data ).

Still, more than 1,000 schools fell short of that threshold, though many of them were for-profit colleges concentrating in short-term credentials rather than traditional four-year degrees.

Seeing whether a college’s former students are earning “reasonable” incomes, said Michael Itzkowitz, HEA Group’s founder and president, can help people weigh whether they want to cross some institutions off their list. Someone deciding between similar colleges, for example, can see the institution that has produced students with significantly higher incomes.

While income isn’t necessarily the only criterion to consider when comparing schools, Mr. Itzkowitz said, “it’s a very good starting point.”

The report used data from the Education Department’s College Scorecard to assess the earnings of about five million former students who had attended about 3,900 institutions of higher education, 10 years after they first enrolled. (The analysis includes data for people who didn’t complete their degree.) The report includes public colleges as well as private nonprofit and for-profit schools; the schools may offer nondegree certificates, associate degrees and bachelor’s degrees.

The analysis found that schools where students earned less than their peers who never attended college were generally those offering nondegree certificates, which can often be completed in 18 months or less, as well as for-profit institutions, although the list also includes some public and private nonprofit schools. At 71 percent of for-profit schools, a majority of students were earning less than high school graduates 10 years after enrolling, compared with 14 percent of public institutions and 9 percent of private nonprofit schools, Mr. Itzkowitz said.

“College is, indeed, worth it,” Mr. Itzkowitz said, but paying for it can be “substantially riskier” depending on the type of school you attend or the credential you seek.

(Another report found that former students of for-profit colleges tend to experience more financial risk than those who attended similarly selective public colleges. Those risks include having to take on more debt for higher education, a greater likelihood of defaulting on student loans and a lower likelihood of finding a job.)

Jason Altmire, president and chief executive of Career Education Colleges and Universities, a trade group representing for-profit career colleges, said lumping together schools offering mainly short-term certificate programs with colleges offering four-year degrees didn’t make sense. People who want to work in certain careers — hairdressing, for instance — generally can’t work in the field unless they earn a certificate, he said.

Mr. Altmire also said that income data from for-profit certificate schools might be skewed by “gender bias” because the programs had a higher proportion of women, who were more likely than men to work part time while raising families, lowering a school’s reported median income.

The HEA report also compared colleges’ performance with other benchmarks, like the federal poverty line ($15,000 annual income for an individual), which is used to determine eligibility for benefits for government programs like subsidized health insurance and Medicaid. Incomes at the “vast majority” of colleges exceeded this cutoff, the report found, although 18 — nearly all of them for-profit schools offering nondegree certificate programs in beauty or hairstyling — had students with median incomes below that threshold.

Majors also matter, since those in science, technology, engineering and nursing typically lead to significantly higher salaries than majors in the arts or humanities. (Last year, HEA published a separate analysis of the college majors that pay the most.)

When comparing the earnings after college, students and families shouldn’t look at the data in a vacuum, said Kristina Dooley, a certified educational planner in Hudson, Ohio. Many schools where former students go on to be top earners have programs focusing on health sciences, technology or business, but that may not be what you want to study.

“Use it as one piece of information,” Ms. Dooley said.

She said that students shouldn’t rule out a college just because it wasn’t at the pinnacle of the income list. Do ask questions, though — like whether its career services office helps with setting up internships and making alumni connections to assist you in finding a good-paying job.

Amy S. Jasper, an independent educational consultant in Richmond, Va., said postgraduate income might matter more to students and families who had to get a loan for college. “How much debt do they want to incur?” she said. “That is something that needs to be taken into consideration.”

But, she said, the benefits of college are not just financial. “I’d like to think that picking the right school is also about becoming a better person and contributing to the world.”

Here are some questions and answers about college costs:

What colleges had the highest median incomes?

Marquee names, like most Ivy League schools, Stanford and the Massachusetts Institute of Technology, are heavily represented at the top of HEA’s analysis. Their students had median incomes of at least $90,000 a decade after enrollment. (A handful of for-profit schools, focused on careers like nursing and digital production, can be found there as well.) But the highest-earning colleges on the list? Samuel Merritt University, a nursing and health sciences school in Oakland, Calif., and the University of Health Sciences and Pharmacy in St. Louis, each with incomes above $129,000. You can see the data on the HEA website .

How much does college cost?

The average estimated “sticker” price for college — the published cost for tuition, fees, housing, meals, books and supplies, transportation and personal items — ranges from about $19,000 a year at a two-year community college to about $28,000 for in-state students at a public four-year university to almost $58,000 at a four-year private college, according to 2022-23 data from the College Board . Some students, however, may pay much less because of financial aid.

Are some college programs required to meet income benchmarks?

A federal “gainful employment” rule , which aims to make career programs more accountable, is scheduled to take effect in July. The new rule, which mostly affects for-profit schools but also applies to certificate programs at all types of colleges, requires schools to show that at least half of their graduates earn more than a typical high school graduate in their state and that their graduates have affordable student loan payments. Colleges that miss either benchmark must alert students that the school could lose access to federal financial aid. Schools that fail the same standard twice in three years will become ineligible for federal aid programs.

A Guide to Making Better Financial Moves

Making sense of your finances can be complicated. the tips below can help..

Credit card debt is rising, and shopping for a card with a lower interest rate can help you save money. Here are some things to know .

Whether you’re looking to make your home more energy-efficient, install solar panels or buy an electric car, this guide can help you save money and fight climate change .

Starting this year, some of the money in 529 college savings accounts can be used for retirement if it’s not needed for education. Here is how it works .

Are you trying to improve your credit profile? You can now choose to have your on-time rent payments reported to the credit bureaus  to enhance your score.

Americans’ credit card debt and late payments are rising, and card interest rates remain high, but many people lack a plan to pay down their debt. Here’s what you can do .

There are few challenges facing students more daunting than paying for college. This guide can help you make sense of it all .

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Before applying to the DPMMS PhD , you are encouraged to discuss informally with possible supervisors. It will help our consideration of your application to know with whom you are interested in working and in what fields. This does not necessarily have to be narrowed down to a single supervisor or research area. 

Potential supervisors for the four year PhD, Cambridge Mathematics of Information (CMI), can be found here . 

Contact details may be found on each supervisor's webpage. You are encouraged to make initial contact by email, and to provide a CV and brief explanation of your areas of interest.

Algebra Algebraic Geometry Analysis and Partial Differential Equations Combinatorics Differential Geometry and Topology Foundations Information and Finance Number Theory Probability Statistics

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Analysis and partial differential equations, combinatorics, differential geometry and topology, foundations, information and finance, number theory, probability.

PhDs in Statistics within the Statistical Laboratory cover a wide range of contemporary challenges in the subject, from theoretical and methodological innovations, to computational developments and applications in many different domains. Prospective applicants are encouraged to make contact with a potential supervisor or supervisors prior to submitting their documents. List of PhD supervisors in Statistics who are willing to consider new students for October 2023 admission:

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Qi Xu Receives Inaugural Statistics Fellowship Award for Methodology Research

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Qi Xu headshot

A new fellowship award to support a statistics graduate student is available through UC Irvine’s Donald Bren School of Information and Computer Sciences ( ICS ). The “UCI Statistics Fellowship Award for Methodology Research” was established in honor of Hal Stern , the former Dean of ICS. Stern was also the founding chair of the Department of Statistics, and he currently serves as UCI Provost and Executive Vice Chancellor.

“The great power of statistics and statisticians is in the ability to develop statistical methods that can help researchers address important science and policy issues,” says Stern. “This award recognizes a Ph.D. student who has contributed novel methodology in one or more application areas.”

On February 27, 2024, before a seminar given by a distinguished statistician Xihong Lin of Harvard University at the Calit2 Auditorium, the inaugural award recipient was announced. Stern honored fifth-year Ph.D. candidate Qi Xu , recognizing his innovative research into statistical learning, causal inference, and differential privacy, particularly as applied in precision medicine and mobile health.

Qi Xu holding an award next to Hal Stern. Behind them, the screen shows the upcoming talk by Xihong Lin on "Empower Whole Genome Sequencing Analysis"

“I am deeply honored and profoundly grateful to be the first recipient of this distinguished UCI Statistics Fellowship Award for Methodology Research, especially given that it honors our esteemed founding chair, Hal Stern,” says Xu. “This recognition not only highlights the importance of innovation and excellence in statistics methodology research, but also pays tribute to a visionary leader whose contributions continue to inspire young generation statisticians and shape our community.”

Xu is making his own contributions through his work to reduce the cost of developing learning algorithms and to help clinicians tailor treatments to individual patient profiles. “My crowdsourcing project improves the process through which observation outcomes are labeled to support the development of supervised learning algorithms, using a robust approach to infer the true labels given noisy and unreliable labels collected from a general (untrained) crowd,” he explains. “Another research line of mine aims to enhance decision-making in precision medicine, specifically finding the best combination therapy considering the complex interaction effects among different therapies.” Xu is also working to advance mobile health by facilitating the identification of new physiological patterns previously elusive in lab settings (his work has appeared in Sleep and Women’s Health journals).

“Xu has been remarkably productive during his Ph.D. studies at UCI, focusing on the development of novel statistical methodologies, theory and machine learning tools with applications in precision medicine, mobile health and bioinformatics,” says his advisor, Chancellor’s Professor Annie Qu . “He has emerged as an independent researcher and a deep thinker with a strong capability for independently developing original methodologies and theories in statistics and machine learning, working on important applications such as mobile health data and DNA methylation study for post-traumatic stress disorder.”

In August, Xu will be joining the Department of Statistics and Data Science at Carnegie Mellon University as a postdoc fellow working with Professor Kathyrn Roeder and Professor Jing Lei.

“His innovative statistical methods will make a substantial impact on the fields of statistics and science,” says Qu, noting that he stands out as one of her most accomplished Ph.D. students in recent years.

“Receiving this fellowship reinforces my commitment to advancing the frontiers of statistical methodology,” says Xu. “It is also an encouragement for me to continue striving for excellence and to contribute meaningfully to our discipline.”

The UCI Statistics Fellowship Award for Methodology Research is funded through contributions from generous supporters, including UCI alumni and ICS leadership council members. Students interested in this and other ICS fellowship opportunities can visit the Fellowships and Funding webpage. Supporters interested in contributing to the endowment for this fellowship can contact Krisit Coyer at [email protected] .

— Shani Murray

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