Modern Statistics and Statistical Machine Learning (EPSRC Centre for Doctoral Training) | University of Oxford
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The Radcliffe Camera
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Modern Statistics and Statistical Machine Learning (EPSRC Centre for Doctoral Training)

About the course

The Modern Statistics and Statistical Machine Learning CDT is a four-year DPhil research programme (or eight years if studying part-time). It will train the next generation of researchers in statistics and statistical machine learning, who will develop widely-applicable novel methodology and theory and create application-specific methods, leading to breakthroughs in real-world problems in government, medicine, industry and science. 

This is the Oxford component of StatML, an EPSRC Centre for Doctoral Training (CDT) in Modern Statistics and Statistical Machine Learning, co-hosted by Imperial College London and the University of Oxford. The CDT will provide students with training in both cutting-edge research methodologies and the development of business and transferable skills – essential elements required by employers in industry and business.

Each student will undertake a significant, challenging and original research project, leading to the award of a DPhil. Given the breadth and depth of the research teams at Imperial College and at the University of Oxford, the proposed projects will range from theoretical to computational and applied aspects of statistics and machine learning, with a large number of projects involving strong methodological/theoretical developments together with a challenging real problem. A significant number of projects will be co-supervised with industry.

The students will pursue two mini-projects during their first year (specific timings may vary for part-time students), with the expectation that one of them will lead to their main research project. At the admissions stage students will choose a mini-project. These mini-projects are proposed by our supervisory pool and industrial partners. Students will be based at the home institution of their main supervisor of the first mini-project.

For students whose studentship is funded or co-funded by an external partner, the second mini-project will be with the same external partner but will explore a different question.

The students will then begin their main DPhil project at the beginning of the third term, which can be based on one of the two mini-projects. Where appropriate for the research, student projects will be run jointly with the CDT’s leading industrial partners, and you will have the chance to undertake a placement in data-intensive statistics with some of the strongest statistics groups in the USA, Europe and Asia.

Alongside their research projects students will engage with taught courses each lasting for two weeks. Core topics will be taught during at the beginning of their first year (specific timings may vary for part-time students) and are:

  • Bayesian Modelling and Computation
  • Statistical Machine Learning; and
  • Modern Statistical Theory.

Students will also be required to take a number of optional courses throughout their four years, which could be made up of choices from the following list: Advanced Monte Carlo methods, Causality and Graphical models, Networks, Nonparametric Bayes, Modern Asymptotics, Optimisation, (Deep) learning Theory and Practice, Reinforcement learning and Multi-Armed Bandits, Applied statistics and Genetics/computational biology.

Supervision

The allocation of graduate supervision for this course is the responsibility of the Department of Statistics (Oxford) and/or the Department of Mathematics (Imperial). It is not always possible to accommodate the preferences of incoming graduate students to work with a particular member of staff. A supervisor may be found outside these departments.

Students are matched to their supervisor for the first mini-project at the start of the course. Within the first year of the course, the student will have the opportunity to work with an alternative supervisor for a second mini-project. It is normal for one of these mini-projects to lead to the full DPhil project with the same supervisory team as was in place for the mini-project chosen.

Assessment

Each mini-project will be assessed on the basis of a report written by the student, by researchers from Imperial and Oxford.

All students will be initially admitted to the status of Probationer Research Student (PRS). Within a maximum of six terms as a full-time PRS student or twelve terms as a full-time PRS student, you will be expected to apply for transfer of status from Probationer Research Student to DPhil status. This application is normally made by the fourth term for full-time students and by the eighth term for part-time students.

A successful transfer of status from PRS to DPhil status will require the submission of a thesis outline. Students who are successful at transfer will also be expected to apply for and gain confirmation of DPhil status to show that your work continues to be on track. This will need to done within nine terms of admission for full-time students and eighteen terms of admission for part-time students.

Both milestones normally involve an interview with two assessors (other than your supervisor) and therefore provide important experience for the final oral examination.

Full-time students will be expected to submit a thesis at four years from the date of admission. If you are studying part-time, you be required to submit your thesis after six or, at most, eight years from the date of admission. To be successfully awarded a DPhil in Statistics you will need to defend your thesis orally (viva voce) in front of two appointed examiners.

The final thesis is normally submitted for examination during the fourth year (or eighth year if studying part-time) and is followed by the viva examination. The final award for Oxford based students will be a DPhil awarded by the University of Oxford.

Graduate destinations

This is a new course and there are no alumni yet. StatML is dedicated to providing the organisation, environment and personnel needed to develop the future industrial and academic individuals doing world-leading research in statistics for modern day science, engineering and commerce, all exemplified by ‘big data’.

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 (including Covid-19), 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.

Other courses you may wish to consider

If you're thinking about applying for this course, you may also wish to consider the courses listed below. These courses may have been suggested due to their similarity with this course, or because they are offered by the same department or faculty.

All graduate courses offered by the Department of Statistics

Entry requirements for entry in 2021-22

Proven and potential academic excellence

Degree-level qualifications

As a minimum, applicants should hold or be predicted to achieve the equivalent of the following UK qualifications:

  • a first-class or strong upper second-class undergraduate degree with honours in mathematics, statistics, physics, computer science, engineering or a closely related subject. 

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

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

If your degree is not from the UK or another country specified above, visit our International Qualifications page for guidance on the qualifications and grades that would usually be considered to meet the University’s minimum entry requirements.

GRE General Test scores

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

Other qualifications, evidence of excellence and relevant experience 

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

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.

English language requirement

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.

Minimum scores required to meet the University's standard level requirement
TestMinimum overall scoreMinimum score per component
IELTS Academic (Institution code: 0713) 7.06.5
TOEFL iBT (Institution code: 0490)100Listening: 22
Reading: 24
Speaking: 25
Writing: 24
C1 Advanced*185176
C2 Proficiency185176

*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.

Supporting documents 

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

Performance at interview

Interviews are held as part of the admissions process for applicants who, on the basis of their written application, best meet the selection criteria.

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

Supervision

Any offer of a place is dependent on the University’s ability to provide the appropriate supervision for your chosen area of work. Please refer to the ‘About’ section of this page for more information about the provision of supervision for this course.

How your application is assessed

Your application will be assessed purely on academic merit and potential, according to the published entry requirements for the course. Students are selected for admission without regard to gender, marital or civil partnership status, disability, race, nationality, ethnic origin, religion or belief, sexual orientation, age or social background. Whether you have secured funding will not be taken into consideration when your application is assessed.

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.

After an offer is made

If you receive an offer of a place at Oxford, you will 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.

Resources

In January 2016 the Department of Statistics moved to occupy a newly-refurbished building in St Giles, near the centre of Oxford. The building has new spaces for study and collaborative learning, including the library and large interaction and social area on the ground floor, as well as an open research zone on the second floor.

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

Starting in the second year, you will teach approximately twelve contact hours per year in undergraduate and graduate courses in your host department. This is mentored teaching, beginning with simple marking, to reach a point where individual students are leading whole classes of ten or twelve undergraduate students. Students will have the support of a mentor and get written feedback at the end of each block of teaching.

Many events bring StatML students and staff together across different peer groups and research groups, ranging from full cohort days and group research skills sessions to summer schools. These events support research and involve staff and students from both Oxford and Imperial coming together at both locations.

The Department of Statistics runs a seminar series in statistics and probability, and a graduate lecture series involving snapshots of the research interests of the department. Several journal-clubs run each term, reading and discussing new research papers as they emerge. These events bring research students together with academic and other research staff in the department to hear about on-going research, and provide an opportunity for networking and socialising.

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

Funding

The University expects to be able to offer up to 1,000 full or partial graduate scholarships across the collegiate University in 2021-22. 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 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 college-specific funding opportunities using the links provided on our college pages.

Further information about funding opportunities for this course can be found on the department's website.

Costs

Annual fees for entry in 2021-22

Full-time study

Fee status

Annual Course fees

Home (UK, Republic of Ireland,
Channel Islands & Isle of Man)
£8,290
Overseas (including EU)£27,460

Part-time study

Fee status

Annual Course fees

Home (UK, Republic of Ireland,
Channel Islands & Isle of Man)
£4,144
Overseas (including EU)£13,731

Course fees are payable each year, for the duration of your fee liability (your fee liability is the length of time for which you are required to pay course fees). For courses lasting longer than one year, please be aware that fees will usually increase annually. For details, please see our guidance on likely increases to fees and charges.

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.

Following the period of fee liability, you may also be required to pay a University continuation charge and a college continuation charge. The University and college continuation charges are shown on the Continuation charges page.

For more information about course fees and fee liability, please see the Fees section of this website. EU applicants should refer to our detailed fee status information and the Oxford and the EU webpage for details of the implications of the UK’s exit from the EU.

Additional information

Full-time study

There are no compulsory elements of this course that entail additional costs beyond fees (or, after fee liability ends, continuation charges) and living costs. However, please note that, depending on your choice of research topic and the research required to complete it, you may incur additional expenses, such as travel expenses, research expenses, and field trips. You will need to meet these additional costs, although you may be able to apply for small grants from your department and/or college to help you cover some of these expenses.

Part-time study

There are no compulsory elements of this course that entail additional costs beyond fees (or, after fee liability ends, continuation charges) and living costs. However, please note that, depending on your choice of research topic and the research required to complete it, you may incur additional expenses, such as travel expenses, research expenses, and field trips. You will need to meet these additional costs, although you may be able to apply for small grants from your department and/or college to help you cover some of these expenses.

Living costs

In addition to your course fees, you will need to ensure that you have adequate funds to support your living costs for the duration of your course.

For the 2021-22 academic year, the range of likely living costs for full-time study is between c. £1,175 and £1,710 for each month spent in Oxford. Full information, including a breakdown of likely living costs in Oxford for items such as food, accommodation and study costs, is available on our living costs page. When planning your finances for any future years of study in Oxford beyond 2021-22, you should allow for an estimated increase in living expenses of 3% each year.

If you are studying part-time your living costs may vary depending on your personal circumstances but you must still ensure that you will have sufficient funding to meet these costs for the duration of your course.

College preference

All graduate students at Oxford belong to a department or faculty and a college or hall (except those taking non-matriculated courses). 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. The Colleges section of this website provides information about the college system at Oxford, as well as factors you may wish to consider when deciding whether to express a college preference. Please note that ‘college’ and ‘colleges’ refers to all 45 of the University’s colleges, including those designated as Permanent Private Halls (PPHs). 

For some courses, the department or faculty may have provided some additional advice below to help you to decide. Whatever you decide, it won’t affect how the academic department assesses your application and whether they decide to make you an offer. If your department makes you an offer of a place, you’re guaranteed a place at one of our colleges. 

How to apply

Before submitting an application, you may find it helpful to contact a potential supervisor or supervisors from among the online profile of StatML academics based in Oxford. This will allow you to discuss the matching of your interests with those of the centre, although there is no guarantee that this specific individual will become your supervisor if you are accepted. More information can be found on the StatML website.

You will need to complete the declaration form once you have applied for this course. More information about this can be found below. 

The set of documents you will be required to submit via our online form comprises the following:

Official transcript(s)

Your transcripts should give detailed information of the individual grades received in your university-level qualifications to date. You should only upload official documents issued by your institution and any transcript not in English should be accompanied by a certified translation.

More information about the transcript requirement is available in the Application Guide.

CV/résumé

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

Statement of purpose/personal statement:
A maximum of 1,100 words

Your statement should be written in English and should specify the broad areas in which your research interests lie -- what motivates your interest in these fields, and why do you think you will succeed in the programme?

The personal statement should describe your academic and career plans, as well your motivation and your scientific interests. When writing your personal statement, please make sure it answers the following questions:

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

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

Your statement will be assessed for:

  • your reasons for applying
  • evidence of understanding of the proposed area of study
  • your ability to present a coherent case in proficient English
  • your commitment to the subject, beyond the requirements of the degree course
  • your preliminary knowledge of the subject area and research techniques
  • your capacity for sustained and intense work
  • your reasoning ability
  • your ability to absorb new ideas, often presented abstractly, at a rapid pace.

References/letters of recommendation:
Three overall, generally academic

Whilst you must register three referees, the department may start the assessment of your application if two of the three references are submitted by the course deadline and your application is otherwise complete. Please note that you may still be required to ensure your third referee supplies a reference for consideration.

Your references should generally be academic, though up to one professional reference will be accepted.

Your references will support intellectual ability, academic achievement, motivation and your ability to work in a group.

Start or continue an application

Step 1: Read our guide to getting started, which explains how to prepare for and start an application.

Step 2: Check that you meet the Entry requirements and read the How to apply information on this page.

Step 3: Check the deadlines on this page and plan your time to submit your application well in advance.

Step 4: Our Application Guide will help you complete the form. It contains links to FAQs and further help.

Step 5: Submit your application as soon as possible (you can read more information about our deadlines).

Step 4: As the admissions process for StatML will be run in parallel with Imperial College London, we ask that you please complete the declaration form once you have applied to one or both of the institutions.

Application GuideApply - FTApply - PTDeclaration Form

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