DPhil in Computational Discovery | University of Oxford
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DPhil in Computational Discovery


The DPhil in Computational Discovery is a multidisciplinary programme spanning projects in Advanced Molecular Simulations, Machine Learning and Quantum Computing to develop new tools and methodologies for life sciences discovery.  

This innovative course has been developed in close partnership between Oxford University and IBM Research. Each research project has been co-developed by Oxford academics working with IBM scientists. Students will have a named IBM supervisor/s and many opportunities for collaboration with IBM throughout the studentship.

The scientific focus of the programme is at the interface between Physical and Life Sciences. By bringing together advances in data and computing science with large complex sets of experimental data more realistic and predictive computational models can be developed. These new tools and methodologies for computational discovery can drive advances in our understanding of fundamental cellular biology and drug discovery. Projects will span the emerging fields of Advanced Molecular Simulations, Machine Learning and Quantum Computing addressing both fundamental questions in each of these fields as well as at their interfaces.

Students will benefit from the interdisciplinary nature of the course cohort as well as the close interactions with IBM Scientists.

Projects accepting applications from May 2020

This course reopened in May 2020 to accept applications for entry in the 2020-21 academic year. The following projects and supervisors are available:

Project A: Algorithm design, analysis and implementation for linear and nonlinear optimisation, convex and nonconvex problems

Academic supervisor: Prof Coralia Cartis (DPhil in Mathematics); IBM co-supervisor: TBC

Project B: Design, analysis, and application of numerical algorithms for information inspired applications in signal & image processing

Academic Supervisor: Prof Jared Tanner (DPhil in Mathematics); IBM Supervisor: TBC

Project C: Using machine learning for efficient Qubit control

Academic supervisor: Dr Natalia Ares (DPhil in Materials); IBM co-supervisor: TBC

Project D: Relationship between quantum and classical computation

Academic supervisor: Niel de Beaudrap (DPhil in Computer Science); IBM co-supervisor: TBC

Project E: Chromatin remodelling and gene regulation in simple eukaryotes, using AI to analyse patterns of gene transcription

Academic supervisor: Prof Jane Mellor (DPhil in Biochemistry); IBM Supervisor: TBC

Project F: Genetics, cell biology and biochemistry in conjunction with advanced microscopy including super-resolution and single molecule imaging as well as computational and bioinformatics methods

Academic supervisor: Prof Ilan Davis (DPhil in Biochemistry); IBM co-supervisor: TBC

Projects accepting applications until Friday 24 January 2020

The following projects and supervisors accepted applications until 12:00 midday (UK time) on Friday 24 January 2020:

Project 1: Mapping the protein “diffusome” of bacteria by high-throughput single-molecule tracking and advanced data analysis

Academic supervisor: Prof Achillefs Kapanidis (DPhil in Condensed Matter Physics); IBM co-supervisor: Dr Simon Colgate

Protein mobility and spatial distribution inside single living bacteria can be explored by single-molecule tracking, uncovering novel information about modes of interactions of proteins in the crowded bacterial cytoplasm. We propose to massively expand our analysis to >1000 bacterial proteins with diverse functions. IBM will support the project via their know-how in algorithms, data analytics, and machine learning.

Project 2: New paradigms for AI-based multidimensional biomedical big-data exploration

Academic supervisor: Prof Ilan Davis (DPhil in Biochemistry); IBM co-supervisor: Dr Flaviu Cipcigan

This project aims to build a new standard for holding multi-dimensional and multimodality informatics and bio-imaging data on GPUs, as a model for clinical genetic and diagnostic data for near instant access and exploration. We aim to develop new paradigms for user interface and conduct exploration of these integrated data sets using the latest cloud computing, gaming technologies and machine learning methods.

Project 3: Combining MD with machine learning to explore cyclic peptides

Academic supervisor: Prof Philip Biggin (DPhil in Biochemistry); IBM co-supervisor: Dr Flaviu Cipcigan

Many diseases cannot be drugged using current small molecule therapeutics and therefore new approaches are urgently needed. One such approach that shows promise is to use cyclic peptides. The aim of this project is to understand the conformational landscape of short cyclic peptides and devise design principles using machine learning for improving pharmacokinetic properties.

Project 4: Development of training algorithms for predicting gene expression outcomes from the distribution of RNA polymerase on genes

Academic supervisor: Prof Jane Mellor (DPhil in Biochemistry); IBM Supervisor: Prof Lior Horesh

The processing, nuclear export and stability of the RNA molecules are pre-determined before transcription. We will use simulated profiles for nascent transcription over human genes to train an algorithm to predict mechanisms and parameters of transcription and gene expression, allowing all the downstream processes to be predicted without experimental determination of each stage.

Project 5: Atomistic modelling of condensed matter on a quantum compute

Academic supervisor: Prof Dieter Jaksch (DPhil in Condensed Matter Physics) and Prof Charlotte Deane (DPhil in Statistics); IBM co-supervisor: Prof Jason Crain and Dr Vadim Elisseev

The description of multipolar quantum fluctuation is a high-dimensional intrinsically complex many-body problem. In this project, we will develop quantum computing algorithms to start tackling such problems and implement them on the current generation of IBM quantum computers. Hardware specific solutions that utilise the available hardware architectures optimally will be developed.

Project 6: Revolutionising chemical synthesis with machine learning

Academic supervisor: Prof Fernanda Duarte (DPhil in Organic Chemistry); IBM co-supervisor: TBC

This project aims to accelerate the discovery of complex drugs and materials by combining organic chemistry principles, quantum chemistry, and machine learning. It will focus on the development of interpretable models that can not only lead to the discovery of new drugs but also provide an understanding of challenging processes leading to their formation and mode of action.

Project 7: Optimisation methods for machine learning

Academic supervisor: Prof Coralia Cartis (DPhil in Mathematics); IBM co-supervisor: Prof Lior Horesh, Dr Soumyadip Ghosh, Dr Songtao Lu

Optimisation problems, of huge scale, form the modelling and numerical core of machine learning and statistical methodologies. A grand challenge in this area is the need to augment stochastic gradient optimisation methods with inexact second-order derivative information, so as to obtain more efficient methods especially in the nonconvex case of deep learning. In this project, we will investigate ways to approximate second-order information in the finite-sum structure of ML optimisation problems.

Project 8: Compiling chemistry / optimisation problems to higher-level anharmonic oscillators

Academic supervisor: Niel de Beaudrap (DPhil in Computer Science); IBM co-supervisor: TBC

This project aims to develop techniques to break down optimisation problems and other problems, using building-blocks consisting of higher-level interactions in superconducting quantum systems.

Project 9: Optimising qubit control with machine learning

Academic supervisor: Dr Natalia Ares (DPhil in Materials); IBM co-supervisor: Dr Ali Javadi-Abhari

This projects aims to achieve automated tuning of semiconductor qubits encoded in gate-defined quantum dots and to use a machine learning approach to enable the tuning of large quantum circuits.

Project 10: New paradigms for dissecting those “black-box” AI models

Academic Supervisor: Prof Jared Tanner (DPhil in Mathematics); IBM Supervisor: Dr. Payel Das and Dr. Pin-Yu Chen

Understanding deep learning (DL) models is of critical importance, as a framework to explain black-box models will chart a path to trustworthy AI. Neural network training relies on the ability to find “good” minimisers of highly non-convex loss functions. There is an urgent need to develop new theoretically grounded framework that can automatically and efficiently decipher the structure-performance (both in terms of generalisation and robustness) of NN models. A similar framework is also needed to understand the impact of hyperparameter settings on model performance, as it is known that well- chosen training parameters (batch size, learning rate, optimiser) produce minimisers that generalise better. Finally, the framework will allow developing models capable of multi-task and multi-environment learning.

Project 11: New paradigms for combining AI and Molecular Simulations for Accelerated Discovery

Academic Supervisor: Prof Philip Biggin (DPhil in Biochemistry); IBM Supervisor: Dr. Payel Das

Data-driven approaches including Machine learning (ML) and deep learning (DL) have shown incredible promises for accelerating scientific discovery in domains such as biology, chemistry, and material science. On the other hand, molecular simulations have come up a long way and being routinely used as a complement or supplement to experiments for validating predictions and providing mechanistic insights to complex processes. There is an urgent need to develop new technology paradigms for automatically and efficiently:

  1. including the feedback from simulations in order to improve the ML model;
  2. optimising the need for additional expensive simulations; and
  3. identifying hidden features from the cheaper simulations that can be a good proxy to learning from expensive simulations.

After a very short induction period of one or two weeks, during which some basic training is provided, you will start a research project in your academic supervisor’s laboratory.
Your supervisor may appoint a senior member of the laboratory as your day-to-day supervisor. Most laboratories have weekly meetings where members present and discuss their research results with other members of the laboratory. You will also regularly present your work in progress seminars, which are attended by other research groups working in related areas. Further support is available from your college advisor.

There are a number of key stages in the research programme:

  1. Within a month of starting, you will meet with your academic and IBM supervisors to finalise your project and agree on an initial programme of research.
  2. Within the first three months, you will complete an analysis of your training needs with your academic supervisor.
  3. After just under one year you will apply to transfer to DPhil status. To do this you write a report describing your research to date and plans for the future. This will be assessed by two independent experts, who interview you as part of the process.
  4. You will apply to confirm your DPhil status by the end of your third year. This involves writing a short progress report and thesis outline and giving a presentation. The application is assessed by two experts.
  5. The final stage is submission of your DPhil thesis, which needs to be done within four years.

Whilst working on your research project you will participate in a comprehensive, flexible skills training programme which includes a range of workshops and seminars in transferable skills, generic research skills and specific research techniques. There are also numerous seminars and lectures by local and visiting scientists and you are provided with many opportunities to meet leading scientists.


The allocation of graduate supervision is the responsibility of the Medical Sciences Graduate School 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 leading the course.

Graduate destinations

It is anticipated that the graduates of this course will have destinations in academic research, in the computing sector, and in end user sectors of machine learning more generally, especially in applications in the life sciences.

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. 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 sabbatical leave, parental leave or change in employment.

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

Other courses you may wish to consider

Applicants are strongly advised to visit the Medical Sciences Graduate School website to help them identify the most suitable course and supervisors.

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.

Biochemistry DPhil
Computer Science DPhil
Condensed Matter Physics DPhil
Materials DPhil
Mathematics DPhil
Organic Chemistry DPhil
Statistics DPhil

All graduate courses offered by the Medical Sciences Divsion

Biochemistry MSc by Research
Clinical Neurosciences MSc by Research
Clinical Neurosciences MSc by Research
Experimental Psychology MSc by Research
Experimental Psychology MSc by Research
Medicine BM BCh (Graduate entry)
Musculoskeletal Sciences MSc by Research
Neuroscience combined MSc and DPhil
Oncology DPhil
Oncology MSc by Research
Pharmacology MSc by Research
Psychiatry MSc by Research
Psychiatry MSc by Research
Radiation Oncology combined MSc and DPhil
Surgical Sciences MSc by Research

Entry requirements for entry in 2020-21

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.

The qualification above should be achieved in one of the following subject areas or disciplines:

  • biochemistry
  • chemistry
  • biology
  • cell biology
  • molecular biology
  • biophysics
  • physics
  • mathematics; or
  • computation.

Please note that entrance is very competitive and most successful applicants have a first-class degree. 

A previous master's degree is not required in order to be considered for the programme.

For applicants with a degree from the USA, the minimum GPA sought is 3.5 out of 4.0. However, entrance is very competitive and most successful applicants have a GPA of 3.7. 

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

GRE General Test scores

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

Other qualifications, evidence of excellence and relevant experience

  • Research or working experience in an area related to your proposed DPhil project would be an advantage.
  • It would be expected that you would be familiar with the recent published work of your proposed supervisor.
  • Whilst not required, publications demonstrating previous research success in a relevant field is likely to advantage your application.

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. 

Detailed requirements - standard level

The minimum scores required to meet the University's standard level are:

IELTS Academic 7.0Minimum 6.5 per component

Minimum component scores:

  • Listening: 22
  • Reading: 24
  • Speaking: 25
  • Writing: 24
Cambridge Certificate of Advanced English (CAE) or C1 Advanced185Minimum 176 per component
Cambridge Certificate of Proficiency in English (CPE) or C2 Proficiency185Minimum 176 per component

Your test must have been taken no more than two years before the start date of your course. For more information about the English language test requirement, visit the Application Guide.

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 normally held as part of the admissions process.  

The main round of interviews is held at the end of January and in early February. Additional interviews may be held at later dates subject to the availability of places.

Applications are reviewed by a panel of academics associated with the course. A short-list of applicants is confirmed, based on assessment of achieved or predicted undergraduate degree grade, academic references, personal statement and CV.

Interviews are in person or by video link/Skype, take approximately 30 minutes, and are conducted by a panel of at least two interviewers. Applicants are asked to talk about any research project(s) that they may have pursued and questioned on aspects of their research training to date, understanding of the proposed area of study and motivation for undertaking a DPhil.


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.

Academic Technology Approval Scheme (ATAS)

Some postgraduate research students in science, engineering and technology subjects will need an Academic Technology Approval Scheme (ATAS) certificate prior to applying for a Tier 4 visa. Further information can be found on our Tier 4 (General) Student visa page. For some courses, the requirement to apply for an ATAS certificate may depend on your research area.


You will have access to:

  • experimental facilities, as appropriate to your research
  • IT support from both the host department for your research and University IT Services
  • library services such as the Radcliffe Science Library and the Cairns Library.

The provision of project-specific resources will be agreed with the relevant supervisor during the planning stages for the research project.


There are over 1,100 full or partial graduate scholarships available across the University. You will be automatically considered for over two thirds of Oxford scholarships, if you fulfil the eligibility criteria and submit your graduate application by the relevant January deadline, with most scholarships awarded on the basis of academic merit and/or potential. To help identify those scholarships where you will be required to submit an additional application, use the Fees, funding and scholarships search and visit individual college websites using the links provided on our college pages.


Annual fees for entry in 2020-21

Fee status

Annual Course fees

Home/EU (including Islands)£7,970

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 dedicated webpage for details of the implications of the UK’s plans to leave the European Union.

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 2020-21 academic year, the range of likely living costs for full-time study is between c. £1,135 and £1,650 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 2020-21, you should allow for an estimated increase in living expenses of 3% each year.


You will have the opportunity to indicate a preference for a college on the application form. If you intend to indicate a preference for a college, you will need to ensure that your preferred college accepts students for the project that is connected to the supervisors that you have chosen. To do this you will need to:

  1. identify the DPhil course that your chosen supervisor usually accepts applications for; and
  2. visit the 'Colleges' tab on the DPhil course page that is connected to your chosen supervisor.

Frequently asked questions

How do I identify which colleges accept applications for my chosen supervisor's subject area?

As part of the application process for this course (outlined on the 'How to Apply' tab), you will be asked to identify a supervisor from the list of projects and supervisors on the 'About' tab. The course that each supervisor usually accepts applications for is shown alongside each supervisor's name. Once you have identified this course, open the relevant course page and access the 'Colleges' tab. You can then decide whether or not to state a preference for one of these colleges.  

What if I make a mistake and indicate a preference for a college that does not accept students in my preferred subject area?

Don't worry. If you are offered a place on the course and your preferred college does not accept students in your supervisor's subject area, the Medical Sciences Doctoral Training Centre will contact you to discuss your college preference.

I have a question about stating a college preference for this course, who can I ask? 

If you have any questions about stating a college preference for this course, please email enquiries@msdtc.ox.ac.uk.

Colleges accepting applications for this course

The colleges listed below accept students for at least one of the projects that are offered as part of this course. You will need to follow the instructions above to identify which colleges accept for your chosen project.

How to apply

You are required to contact a potential supervisor in the first instance in order to discuss the area of research you wish to carry out during the DPhil, and to establish whether they are able to supervise your proposed project. Potential supervisors can be identified by referring to the list of projects and supervisors on the 'About' tab.

Once you have identified and contacted potential supervisors, you will need to enter the full name (given/first name and family name/surname) of your proposed supervisor(s) in the relevant box on the 'Courses' tab of the application form.

The set of documents you should send with your application to this course comprises the following:

Official transcript(s)

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

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


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

Statement of purpose/personal statement:
Up to 1,000 words

The personal statement should be written in English and should focus on your interest in, and experience of your intended research field (rather than personal achievements, interests and aspirations).

In your statement, please include:

  • details of up to three potential supervisors affiliated with the programme whose research is of interest to you;
  • in brief, the research areas and experimental approaches that you would wish to explore in a DPhil research project; and
  • how your academic/research background relates to your proposed study and career plans.

This will be assessed for your reasons for applying, evidence of motivation for and understanding of the proposed area of study, and ability to present a reasoned case in English.

Please note that you are not expected to outline the intended research project in your personal statement.

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.

References should generally be academic though a maximum of one professional reference is acceptable where you have completed an industrial placement or worked in a full-time position. 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).

Application GuideApply

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