DPhil in Computational Discovery
About the course
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 course is taking part in a continuing pilot programme to improve the selection procedure for graduate applications, in order to ensure that all candidates are evaluated fairly. For this course, the socio-economic data you provide in the application form will be used to contextualise the shortlisting and decision-making processes where it has been provided. Please carefully read the instructions concerning submission of your CV/résumé in the How to apply section of this page, as well as the full details about this pilot.
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 while 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. 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.
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.
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.
Projects available for entry in 2023-24
You may identify up to three projects to be considered for in your application.
Further information about the following projects, including references and supervisor contact details can be found on the department's website.
Robustness and Generalisation of Graph Machine Learning Models
Xiaowen Dong and Mihai Cucuringu
Can we predict whether a molecule can be a potent drug against certain bacteria from its chemical structure? Can we detect whether a piece of news on a social media site corresponds to misinformation from its spreading pattern? These are just some of the big questions facing today’s society that can potentially be solved by an emerging area of research: graph machine learning (GML). A key open challenge in GML is the lack of understanding of model robustness and generalisation capability with respect to a perturbation of the graph data, which may come from natural noisy data characteristics (eg spurious correlations or irrelevant information) or malicious attempts (eg adversarial attacks).
In this project, building on top of recent preliminary results from Oxford and IBM, we propose to address this challenge through the lens of graph topology and spectral analysis, and study the implication of perturbations that action in both the graph spatial domain (eg add or delete graph edges) and the graph spectral domain (eg via modification of eigenvalues/eigenvectors of the graph Laplacian) for robustness and generalisation capability.
The expected outputs of this project include:
- developing theoretical foundations for characterizing robustness and generalization of GML;
- devising novel efficient algorithms for improving GML;
- identifying suitable real-world application domains; and
- contributing to joint publications at top AI/ML conferences and patents.
Accelerated Design of New Sustainable Battery Materials with Artificial Intelligence Methods
The provision of sustainable low-carbon energy is among the most urgent challenges of our time, and poses fundamental, exciting scientific questions. Materials performance lies at the heart of the development of green energy technologies, and computational methods now play a vital role in modelling the properties of energy materials. However, a full understanding of the atomistic processes within materials and across interfaces that control the performance of energy storage devices such as lithium-ion batteries remains incomplete. Emerging artificial intelligence (AI) and machine learning techniques are powerful tools offering innovative capabilities for studying new battery materials on length scales from individual atoms to tens of nanometres, promising quantum-mechanical accuracy and predictive power, whilst being many orders of magnitude faster than conventional methods.
The vision of this project is the innovative use of cutting-edge machine learning simulation techniques to probe the atomic-level operation of battery materials, thereby enabling a previously missing microscopic understanding and an accelerated design of new sustainable materials with enhanced performance. We will address electric vehicle application objectives of increasing the energy density and charge rates of battery electrodes and solid electrolytes, with a particular focus on how their macroscopic properties can be connected to the microscopic structure.
The project will involve the creation of accurate fitting databases and machine-learning-based interatomic potentials to model the underlying atomistic behaviour of novel battery electrodes and solid electrolytes. No equivalent concerted AI-modelling project on battery materials that inter-links such different expertise is being undertaken within any current IBM-Oxford Studentship project.
Quantum Relativistic Simulations for Dense Plasma Systems
The equations governing quantum mechanics have been known for nearly 100 years, but even today the task of reconciling them with the equations of classical motions remains unsolved. The problem is usually expressed in terms of the trajectory (or path) that a particle follows. While in ordinary Newtonian theory a particle moves along a well-defined path, this is not true anymore in orthodox (or the Copenhagen interpretation of) quantum mechanics. This is because it is impossible to simultaneously define the position and momentum of the particle. There is, however, an alternative possibility, called Bohmian mechanics or the de Broglie-Bohm interpretation, whereby the particle follows a definite path.
The Bohmian mechanics approach also offers another advantage. Since, as pointed out by de Broglie, the inclusion of quantum effects are entirely equivalent to the change of the spacetime metric, all relativistic effects in the many-body particle interactions can be included by rewriting and solving the equations of motion in this modified metric. This framework will be important for studying high-temperature dense plasmas as those found in the interior of stars but also in inertial fusion experiments. Moreover, as the proposed approach treats both electrons and ions on an equal footing, the plasma response to perturbations inclusive of both electron and ion dynamics can be fully calculated.
The student’s primary task will consist in developing this new computational approach within a Molecular Dynamics framework. The student will initially focus on the numerical implementation and then apply that to realistic systems – ie for the calculation of transport coefficients in inertial fusion experiments. We expect this project to produce important results that are of interest not only to the fusion community, but the broader community of researchers working in high energy density physics, planetary science and extreme materials.
Effective Transport Coefficients in Extreme Dynamic Material
Characterizing and quantifying mass, momentum, and energy transport in materials under extreme conditions is vital in many areas of research, ranging from inertial confinement fusion to the behaviour of matter in the interiors of giant planets and stars. With temperatures of a few electron volts (eV) and densities comparable to solids, warm dense matter (WDM) forms a key constituent of planetary interiors as well as cooler stellar objects such as brown dwarfs and the crust of neutron stars. Transport properties are difficult to model in WDM. Our goal here is to develop an experimental and numerical framework that can be used to measure effective transport in WDM and then use the experimental data to construct a suitable representation via symbolic regression or a trained neural network.
Our proposed work utilises recent advances in diagnostics and in machine learning. For the experiments, we intend to use X-ray photon correlation spectroscopy (XPCS) in novel ways to extract effective transport coefficients in dynamic laser-driven materials. Here we want to propose a novel machine learning approach to address the complex micro-physics of material strength properties and to identify their emergent behaviour via closed mathematical expressions. This is done by using a Graph Neural Network (GNN) to represent the discrete description of the underlying continuum system and then applying deep learning techniques to obtain a representation of the material properties as a function of the state variables (density, temperature, etc).
Our long-term goal is the development of augmented methods to ultimately improve the design and verification integrated modelling of WDM systems, in the sense that fluid simulations using these effective transport coefficients may now be able to capture the relevant physical processes at all scales. We expect this project to produce important results that are of interest not only to the fusion community, but the broader community of researchers working in high energy density physics, planetary science and extreme materials.
ML-Driven Fragment-Based Drug Development Using Data From High-Throughput X-Ray Crystallography and Biophysical Measurements
Charlotte Deane and Frank Von Delft
An approach will be developed that integrates machine learning with experimental measurements for the rapid design of bioactive compounds that are suitable as chemical probes. Chemical probes are often used as the starting point for drug discovery campaigns and can help elucidate the mechanism of molecule-target interactions. Machine learning techniques will be developed that use structural data as input to suggest potent, synthetically tractable molecules to make within this workflow. Techniques that better deconvolve signal from noise in biophysical assays will also be investigated.
This project aims to generate an algorithmic formalism that achieves rapid design of bioactive compounds suitable as chemical probes. We will develop a machine learning approach that iteratively integrates experimental data from low-cost robotic organic synthesis, high-throughput crystallography (XChem), and rapid sensor-based biophysical measurements (Grating-Coupled Interferometry). The engine will be able to suggest new molecules that are potent, synthetically tractable and have good pharmacological properties.
This approach builds on methodological discoveries made in the successful COVID Moonshot initiative, an open science consortium that Professor von Delft co-founded, which delivered preclinical candidates against SARS-CoV-2 main protease from fragment hits in 18 months with <£1m.
This project will address the two interrelated questions that must be answered for these proof-of-concept successes to become an effective platform for probe discovery. First, how can machine learning take structural biology data as input to suggest new molecules to make? Secondly, how can we deconvolve signal amid noise in biophysical assays when the input is a crude reaction mixture?
Taking the Structure of Proteins Into Account: Predicting if Infections are Resistant to β-lactam Antibiotics Using Graph-Based Convolutional Neural Networks
This project will use machine learning to predict whether novel protein mutations confer resistance to specific antibiotics. Antimicrobial Resistance is a great concern to modern medicine. The Fowler group has previously used simple machine learning and physical simulation to address this problem. We aim to use graph neural network techniques developed by the IBM team to create advanced deep learning models and then train, validate and test the models on the large datasets we possess.
Antimicrobial Resistance (AMR) is a growing threat to modern medicine; not only would infectious disease claim many more lives than it already does but it would adversely impact many treatments that rely on prophylaxis, eg many anti-cancer therapies.
Due to the much higher degree of genetic variation and to maximise the information derived from the clinical samples, the student will work closely with IBM Research to create and then train graph-based convolutional neural networks that capture the topology of the protein and the protein in complex with ligands in the connectivity of the hidden layers. Crucially, such models can be trained from gene sequences rather than mutations and so can innately deal with the greater genetic variation. These techniques incorporate structure-based approaches within a deep learning framework and have been successfully validated and applied to the binding of proteins to small molecules and biologics.
This work will also utilize open source software. New developments are intended to be contributed to open source efforts.
The project would suit a student wishing to learn and apply machine learning to an important biomedical problem who wants to work in a fast-paced and highly interdisciplinary environment. It could therefore suit students from a broad range of backgrounds.
Defining Computation and Connectivity in Neuronal Population Activity Underlying Motor Learning
Neural network structure constrains the activity dynamics of the brain. Specifically, learning of movements guided by the outcome of previous actions leads to adaptations in the motor cortical network and its activity. To understand these mechanisms on the cellular level would require simultaneous recordings from hundreds of local neurons at millisecond timescale in vivo during learning of a skilled movement. We have successfully established an approach to simultaneously record thousands of neurons across motor regions in mice, using recently developed high-density electrode silicon-probes in combination with machine-learning based kinematic analysis and cell-type specific optogenetic modulation.
Motivated by recent work that links structure of population activity to the underlying synaptic connectivity (Dahmen et al., 2022) and our experience in cortical microcircuits (Peng et al., 2019, 2022), we aim to identify core changes in neuronal microcircuits that underlie motor learning and execution. We will develop novel approaches to extract activity signatures reflecting plastic changes on the local synaptic level and model how these constrain the overall dimensionality of neuronal population activity. The results will provide a microcircuit level understanding of learning in motor circuits and lay the groundwork to study neural network architecture in high-density electrophysiological recordings.
Optimising Therapy for Brain Disorders Through AI-Refined Deep Brain Stimulation
Brain stimulation is extensively used to modulate neural activity in order to alter behaviour. In recent years, closed-loop stimulation techniques have gained increasing traction to sense a biomarker such as elevated neural activity patterns, and deliver stimulation in time with such events. Closed-loop stimulation techniques are used both for establishing a causal link between behaviour and neural activity, and also to treat various neurological and psychiatric conditions.
Building on our recent work (West et al 2022, Cagnan et al 2017), this PhD project aims to formalise stimulation parametrisation by using theoretical models of brain circuits in combination with state of the art machine learning approaches. Specifically, we will train artificial neural networks to classify discrete brain states of interest and optimise stimulation parameters to achieve precise manipulation of activity propagating across brain circuits. The successful development of such an approach would provide a powerful framework to guide next generation stimulation strategies both for usage in basic science and clinical applications.
Foundations of Stochastic Gradient Descent (and Generalization)
Stochastic gradient descent is one of the most widely used algorithmic paradigms in modern machine learning. Despite its popularity, there are many open questions related to its generalization capabilities. For instance, while there is preliminary evidence that early-stopped gradient descent applied to over-parameterized models is robust with respect to label mispecifications, a complete theory that can account for this phenomenon is currently lacking.
The goal of this project is to rigorously investigate the robustness properties of early-stopped gradient descent from a theoretical point of view in simplified settings involving linear models, and to establish novel connections of such a methodology with the field of distributionally-robust optimization. The project will combine tools from the study of random structures in high-dimensional probability (eg concentration inequalities, theory of optimal transport) with the general framework of gradient and mirror descent methods from optimization and online learning (eg regularization).
Modelling Proton Delocalization in Hydrogen-Bond Networks with Quantum Simulators
A new way of studying biochemical structures and extending computational models is through quantum information and quantum simulators/computers. An example of a widely relevant use-case for quantum simulators in biochemistry and pharmacology involves the study of tautomerization – when protons transfer between molecular sites resulting in different configurations of a chemical compound. Tautomerization plays an important role in canonical biochemical reactions by providing a pathway that can enable catalytic enzyme reactions, determine the tautomeric forms of photoproteins in luminescence or the aromatization of molecules. The molecular structures and their dynamics can be modelled as open quantum systems for improved accuracy. Traditional quantum chemistry assumes that protons are fixed and presently lacks the tools to model proton delocalization and decoherence processes to describe the dynamics of these open systems. Quantum simulators have the potential to model systems more accurately and to offer new ways of thinking and understanding biochemical processes.
Accelerated Modelling of Reaction Pathways using Machine Learning for Carbon Capture Materials
The climate crisis, due to anthropogenic emissions, is likely the single largest issue facing the planet in the 21st Century. Materials to aid in curbing, and ultimately removing, carbon emissions are vital to prevent unsustainable climate change. To meet this challenge, novel materials and new capabilities are required. Accurate and reliable predictions of material capabilities are a critical part in material design. Computational methods play an increasing role in providing these capabilities prior to laboratory work. This proposal seeks to address the issue of accurately, reliably, and efficiently computing reaction pathways and using such methods to improve carbon capture materials.
There are numerous carbon capture materials proposed, but only a handful are commercially operating to date. Additionally, for these materials to be used in a cyclic manner, the carbon dioxide must be removed, and the material reformed in an economical and sustainable manner.
The project will develop accurate and efficient Machine Learning (ML) models to elucidate reaction pathways and evaluate novel designs. Where data exist in the open literature, we will use this, but we will also seek to generate our own data specific to the domain at hand, which currently lacks such data sets. The overall aim of this project will be to develop ML models applicable to describing chemical reaction pathways. We will show these models operating on well-known and understood systems prior to using them to improve the current generation of carbon capture materials.
Using Carbon Dioxide to Make Plastics and Materials – Circular Carbon Economies
The project combines a long-standing expertise in both IBM and Oxford Chemistry, Williams Research group, into ensuring the next generation polymers and plastics maximise carbon dioxide recycling and minimize negative environmental impacts. This will be achieved through investigation of impacts throughout the lifecycle – from ensuring the monomers used are bio-derived or even waste carbon dioxide, to delivering efficient manufacturing processes, to designing polymers with the highest performances to minimize need for additives to ensuring all materials are recyclable after use.
Research will exploit discoveries in the Williams group allowing carbon dioxide and bio-derived monomers to be transformed into polymers, plastics and elastics. The project focusses on efficient catalysis to make carbon dioxide derived thermoplastics for use in future electronics and electrical applications, including as an insulator and in heat management systems, which show lower greenhouse gas emissions throughout their life cycles.
The project will involve a period of secondment and close collaboration with the IBM Almaden (San Francisco) team headed up by Jim Hedrick. The research at IBM focusses on using continuous flow methods in polymer synthesis to accelerate manufacturing and improve control over polymer properties. Polymer property and processing assessments will be made between the laboratories at the University of Oxford and IBM Almaden.
Developing Geospatial Foundation Models for Climate Model Evaluation and the Detection of Extreme Climate Events
Foundation models are a general class of AI models trained (generally self-supervised) on a large set of multimodal data. The resulting foundation model can be fine-tuned to solve a wide array of downstream tasks. Despite the methodology is general and applicable to different domains and applications, current popular examples are mostly focused on natural language processing (eg GPT-3 for natural language and Dall-E for text-to-image tasks).
As foundation models are complex and trained on large datasets, they tend to exhibit an emergent property where a system’s behaviour is implicitly induced rather than explicitly specified. This is especially advantageous for many applications in climate science where the underlying physical processes are sometimes too complex for a limited amount of data to capture, or high quality data for training models able to detect climate events of interest might be scarce. The aim of this PhD project is to develop new modular deep learning architectures for foundation models that allow one to deal with the multivariate nature of climate data and its spatio-temporal intermittence.
The project will explore transformer-based architectures to allow parallelization between modalities before the extracted data representation is recombined. Focusing on training efficiency and computation, the project may also investigate whether it is possible to understand the added value of bringing in an additional modality or sets of training samples. Ultimately, the foundation models developed during the project will be tested and compared to the regular paradigm (eg developing a bespoke model for each application), in downstream tasks. This might include earth observation for climate hazards (eg flood, wildfire, landslide, drought) and climate model evaluation against observations. If successful, these foundation models will be an extremely powerful tool that will enable more efficient and accurate climate impact assessment and earth observation.
Advancing Synthesis Prediction with Machine Learning - A Data Driven/Mechanistic Approach
Professor Fernanda Duarte and Dr Teodoro Laino
The project will apply the latest machine learning (ML) techniques to chemical applications, including the exploration of reaction pathways toward medicinally relevant scaffolds. The aim will be to develop interpretable ML algorithms that facilitate the prediction of synthetic routes and provide a mechanistic understanding of their outcome.
This project will enable the student to explore fundamental scientific questions at the interface of chemistry and machine learning and apply these insights to tackle timely real-world applications. It will also provide the opportunity to work with multi-disciplinary teams in academia and industry. The group of Professor Fernanda Duarte will provide world-leading expertise in reaction pathway modelling and automation, while the team at IBM Research will bring expertise in the development of computational chemistry software and AI techniques.
Applicants must have, or expect to obtain, a Master’s (or equivalent) degree in Chemistry, Physics, Computer Science or a related subject. Previous experience in computational chemistry or machine learning would be an advantage. The successful candidate will be based at the University of Oxford and, as part of their project, will spend at least three months working with at IBM Research in Zurich, Switzerland.
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.
Your supervisor may appoint a senior member of the laboratory as your day-to-day supervisor. Further support is available from your college advisor.
The frequency of meetings with supervisors will depend on which department your DPhil is based. Commonly, within those departments based with the Medical Science Division, once a fortnight is typical.
There are a number of key stages in the research programme. 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. Within the first three months, you will complete an analysis of your training needs with your academic supervisor.
Students begin the DPhil in Computational Discovery programme as a probationary research student (PRS). Before the end of their fourth term students are required to apply for Transfer to DPhil Status.
A successful transfer of status from PRS to DPhil status will require the submission of a transfer report. Students who are successful at transfer will also be expected to apply for and gain confirmation of DPhil status to show that their work continues to be on track. This will need to done within nine terms of admission.
Both milestones normally involve an interview with two assessors (other than your supervisor) and therefore provide important experience for the final oral examination.
You will be expected to submit an original thesis of up to 50,000 words after three or, at most, four years from the date of admission. To be successfully awarded a DPhil you will need to defend your thesis orally (viva voce) in front of two appointed examiners.
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. 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
You are strongly advised to visit the Medical Sciences Graduate School website to help 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.
Courses closely related to the DPhil in Computational Discovery
Computer Science DPhil
Condensed Matter Physics DPhil
Organic Chemistry DPhil
All graduate courses offered by the Medical Sciences Division
Entry requirements for entry in 2023-24
Proven and potential academic excellence
As a minimum, applicants should hold or be predicted to achieve the following UK qualifications or their equivalent:
- a first-class or strong upper second-class undergraduate degree with honours.
The qualification above should be achieved in one of the following subject areas or disciplines:
- cell biology
- molecular biology
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(s) would be an advantage.
- Whilst not required, publications demonstrating previous research success in a relevant field is likely to advantage your application.
English language proficiency
This course requires proficiency in English at the University's standard level. If your first language is not English, you may need to provide evidence that you meet this requirement. The minimum scores required to meet the University's standard level are detailed in the table below.
|Test||Minimum overall score||Minimum score per component|
|IELTS Academic (Institution code: 0713)||7.0||6.5|
TOEFL iBT, including the 'Home Edition'
(Institution code: 0490)
*Previously known as the Cambridge Certificate of Advanced English or Cambridge English: Advanced (CAE)
†Previously known as the Cambridge Certificate of Proficiency in English or Cambridge English: Proficiency (CPE)
Your test must have been taken no more than two years before the start date of your course. Our Application Guide provides further information about the English language test requirement.
Declaring extenuating circumstances
If your ability to meet the entry requirements has been affected by the COVID-19 pandemic (eg you were awarded an unclassified/ungraded degree) or any other exceptional personal circumstance (eg other illness or bereavement), please refer to the guidance on extenuating circumstances in the Application Guide for information about how to declare this so that your application can be considered appropriately.
You will need to register three referees who can give an informed view of your academic ability and suitability for the course. The How to apply section of this page provides details of the types of reference that are required in support of your application for this course and how these will be assessed.
You will be required to supply supporting documents with your application, including an official transcript and a CV/résumé. The How to apply section of this page provides details of the supporting documents that are required as part of your application for this course and how these will be assessed.
Performance at interview
Interviews are normally held as part of the admissions process.
It is anticipated that interviews for this course will be held in mid- to late March.
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 conferencing software, 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.
How your application is assessed
Your application will be assessed purely on your proven and potential academic excellence and other entry requirements published under that heading. References and supporting documents submitted as part of your application, and your performance at interview (if interviews are held) will be considered as part of the assessment process.
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.
Whether or not you have secured funding will not be taken into consideration when your application is assessed.
Initiatives to improve access to graduate study
This course is taking part in a continuing pilot programme to improve the selection procedure for graduate applications, in order to ensure that all candidates are evaluated fairly. For this course, the socio-economic data you provide in the application form will be used to contextualise the shortlisting and decision-making processes where it has been provided. Further details about this pilot, which applies to all applicants to this course, can be found in our pilot selection procedures section.
Processing your data for shortlisting and selection
Admissions panels and assessors
All recommendations to admit a student involve the judgement of at least two members of the academic staff with relevant experience and expertise, and must also be approved by the Director of Graduate Studies or Admissions Committee (or equivalent within the department).
Admissions panels or committees will always include at least one member of academic staff who has undertaken appropriate training.
Other factors governing whether places can be offered
The following factors will also govern whether candidates can be offered places:
- the ability of the University to provide the appropriate supervision for your studies, as outlined under the 'Supervision' heading in the About section of this page;
- the ability of the University to provide appropriate support for your studies (eg through the provision of facilities, resources, teaching and/or research opportunities); and
- minimum and maximum limits to the numbers of students who may be admitted to the University's taught and research programmes.
Offer conditions for successful applications
If you receive an offer of a place at Oxford, your offer will outline any conditions that you need to satisfy and any actions you need to take, together with any associated deadlines. These may include academic conditions, such as achieving a specific final grade in your current degree course. These conditions will usually depend on your individual academic circumstances and may vary between applicants. Our After you apply pages provide more information about offers and conditions.
In addition to any academic conditions which are set, you will also be required to meet the following requirements:
If you are offered a place, you will be required to complete a Financial Declaration in order to meet your financial condition of admission.
Disclosure of criminal convictions
In accordance with the University’s obligations towards students and staff, we will ask you to declare any relevant, unspent criminal convictions before you can take up a place at Oxford.
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 Student visa (under the Student Route). 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.
All applicants who are offered a place on the DPhil in Computational Discovery course will be offered a fully-funded scholarship from EPSRC iCASE, covering course fees for the duration of their course and a living stipend. Please see the Graduate School website for further details about funding for this course.
Annual fees for entry in 2023-24
Annual Course fees
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.
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.
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.
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 2023-24 academic year, the range of likely living costs for full-time study is between c. £1,290 and £1,840 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 2023-24, it is suggested that you allow for potential increases in living expenses of 5% or more each year – although this rate may vary significantly depending on how the national economic situation develops. UK inflationary increases will be kept under review and this page updated.
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.
The following colleges accept students on the DPhil in Computational Discovery:
Before you apply
Our guide to getting started provides general advice on how to prepare for and start your application. Check the deadlines on this page and the information about deadlines in our Application Guide. If it's important for you to have your application considered under a particular deadline – eg under a December or January deadline in order to be considered for Oxford scholarships – we recommend that you aim to complete and submit your application at least two weeks in advance.
Application fee - waived for all applications to this course
The application fee of £75, which is usually payable per course application, will be waived for all applications to this course. When selecting application type, please choose 'Standard'. When you submit your application you will not be shown the screen that collects payment details and you will not need to enter a waiver code.
Do I need to contact anyone before I apply?
Questions about projects can be directed to the supervisor listed for the project. Contact details for supervisors are listed on the departmental website.
Completing your application
You should refer to the information below when completing the application form, paying attention to the specific requirements for the supporting documents. If any document does not meet the specification, including the stipulated word count, your application may be considered incomplete and not assessed by the academic department. Expand each section to show further details.
Proposed field and title of research project
Once you have identified up to three projects that you would like to work on, you will need to enter these in the 'Field and title of research project field', making sure you identify them by number, eg Project 6.
You are not required to name a proposed supervisor. This field can be left blank.
Three overall, academic and/or professional
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.
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.
Full instructions and link to standard CV creation form
A CV/résumé is compulsory for all applications. You will need to upload a standardised CV to the graduate application form as part of your application. This standardised CV should be generated using the online form that requests certain information that you will likely have included on your CV. Once you have completed the form, you will have 15 minutes to download your CV as a PDF document.
This PDF document will be in the same format for all applicants and you should not modify the document before you upload it, or submit your CV in a different format.
Full instructions and a link to the standard CV creation form are provided on the Medical Sciences Division website via the button above. The instructions page contains links to example clinical and non-clinical CVs, with details of what to include and suggested answer formats.
If you require help or advice while generating your CV using the online form, please contact the Medical Sciences Graduate School for assistance (firstname.lastname@example.org).
You can find more information about the standard CV form on our page that provides details of the continuing pilot programme to improve the selection procedure for graduate applications.
Statement of purpose/personal statement:
A maximum of 500 words
You should provide a statement of your research interests, in English, describing how your background and research interests relate to the programme. If possible, please ensure that the word count is clearly displayed on the document.
The statement should focus on academic or research-related achievements and interests rather than personal achievements and interests.
This will be assessed for:
- your reasons for applying;
- evidence of motivation for and understanding of the proposed area of study;
- the ability to present a reasoned case in English;
- capacity for sustained and focused work; and
- understanding of problems in the area and ability to construct and defend an argument.
It will be normal for students’ ideas and goals to change in some ways as they undertake their studies, but your personal statement will enable you to demonstrate your current interests 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.