Fundamentals of AI (EIT CDT)

Training will begin with an immersive module in software engineering that will lay the foundation for a year-long, team-based open-source software development project. This course introduces software engineering and is offered to all 1st year students at the MPLS Doctoral Training Centre. It covers the main software architecture paradigms: procedural, object-orientated and functional programming, version control with Git, testing and continuous integration, project packaging and containerisation, an intro to using HPC clusters and computational workflows with snakemake.

This module provides a rapid introduction to key topics that underpin modern artificial intelligence research. The objective is to provide an understanding of the fundamental concepts that underpin the current state of the art and to be able critically assess how their DPhil can contribute to advancing knowledge. The module will provide students with an awareness of a breadth of concepts that will allow them to make connections between areas during their DPhil and beyond. Topics will include areas such as Learning theory, Bayesian methods, Reinforcement Learning and Diffusion Processes.

This module introduces you to current research developments at the interface between Statistics and AI, while also providing an opportunity to interact with module leaders and ECRs from the Department of Statistics and engage with their research areas and interests. The module will cover topics such as Bayesian Uncertainty Quantification, Statistical Wrappers for Black-Box ML Methods and Deep Generative Modelling.

These will be short sessions led by the leading academics in our supervision pool. They will expose you to some of the cutting-edge research in AI at the University and give you opportunities to connect with the researchers. Many of you may have ideas about what you want to do now, but we hope these sessions will highlight areas and topics that you have not considered before and trigger new ideas.

These sessions will be looking at areas such as data management, high-performance computing, research publishing, ethics and regulation.

Foundations

Researchers in this domain are primarily focused on deep mathematical analysis for the development and further understanding of concepts that have potential broad application to AI such as learning theory, optimisation and stochastic analysis. They may also undertake mathematical analysis of AI methods whose utility have been demonstrated through empirical studies but where theoretical insight has been absent.

Applied

Researchers in this domain are inspired by real-world problems and will focus on developing substantial modifications of foundational concepts to match the particular needs of applications examining issues such as multimodal data integration, missing data, experimental design and causality. They may consider existing heuristically designed AI methods that have demonstrated high performance in applications and reformulate using foundational concepts to improve and extend the use of these approaches.

Systems

Researchers in this domain are concerned with the design, deployment and/or maintenance of large scale AI systems. They could apply formal analysis to understand the properties of such AI systems or substantially adapt and develop foundational concepts to assist in the design of better systems. Research may address topics such as scalability, resource use, safety and algorithmic fairness. 

Early on in your first term, you will work in a team to consider a substantive AI problem that you will work on together throughout the first year. This will be a chance for you to engage closely with EIT teams early in your DPhil. These projects will be one of your main occupations during the second term of the first year. Time will be set aside to continue working on these projects during the third term and the summer alongside your individual projects.

You will undertake two individual rotation projects between April and September (with time set aside to keep the group projects going). These will be carried out under the supervision of academics from the supervisor pool but you will be encouraged to include additional co-supervisors from EIT. Additional co-supervision may also be arranged with Oxford academics beyond the supervisor pool. During this time, you will be based in the home department of the rotation project supervisors.

In the second year, you will move to the academic department of your main supervisor and commence your main research project. 

Examples of research projects can be found on the department's website.

Students in the CDT will have access to EIT leadership and innovation training to support them through to graduation and beyond. The programming is built around key principles that nurture holistic growth and empower students to lead with purpose and innovation: Leadership Development, Innovation and Entrepreneurship, Community and Personal and Lifelong Development. The programme aspires to cultivate a lifelong network of impact-driven leaders and innovators. The training emphasises self-leadership, personal values and the skills needed to lead others and systems. The programme will provide students with access to top innovators and experts, along with opportunities to learn both the theory and practice of entrepreneurship. The training is delivered through a variety of formats, including expert talks, practical workshops and peer discussions.

Studying for a DPhil requires an aptitude for original, independent and critical thinking, as well as the ability to write papers, present data and manage projects. The Research and Professional Skills programme, which runs throughout the four years at the Doctoral Training Centre, is designed not only to equip students with the necessary skillset to carry out their research at Oxford but also provides an opportunity to develop personal transferable skills. Through a series of lectures and seminars students will be able to improve their competence and confidence as researchers. These qualities are not only key in research but equally important in many careers. Research and Professional Skills training covers scientific methodology, public engagement with science, reading scientific literature across disciplines, scientific writing, poster production, publishing a research paper, presentation and communication skills, management skills, managing your DTC DPhil, IP and commercialisation of research, research ethics, introduction to entrepreneurship, interview techniques and career development.

All students will receive a laptop.

You may have opportunities to access EIT resources and are encouraged to work closely with EIT research teams with possibilities to spend time on site at their hubs.

When you move out to your department you will also have access to the facilities provided by that department. You will remain a member of the CDT and will be able to return to the MPLS Doctoral Training Centre (MPLS DTC), on Keble Road, to use the facilities there.

EIT's AI researchers will be contributing towards the individually-tailored training in advanced AI techniques and software development as part of the CDT's wider training efforts. EIT researchers are engaged in internationally leading scientific research projects underpinned by cutting edge development of AI and machine learning techniques which will engage, educate and support students in their learning.

In the event of the need for pastoral care, support is available from your college, from the project supervisor, the MPLS DTC, and the CDT management team. You will have access to seminars in all four departments as well as across the wider university.

In addition to the training modules offered by the CDT, you will be able to sign up for a wide range of training courses and modules offered by departments across the university via the University's Researcher Training Tool. You will also have access to Oxford's wide library network, including the recently refurbished Radcliffe Science Library.

EIT Leadership and Innovation Programming Training Students in the CDT will also have access to EIT leadership and innovation training to support them through to graduation and beyond. With a core focus on skills building, this programming is designed to develop leadership in the context of science and technology. The programme encourages students to think critically about their role as future scientific leaders and innovators. Through a combination of expert talks, practical workshops, and peer discussions, students will have the opportunity to learn directly from leading entrepreneurs and innovators representing a diverse range of sectors.

The allocation of graduate supervision for this course is the responsibility of the EIT CDT in Fundamentals in AI 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 CDT.

During your first year, you will be allocated a supervisor from the CDT's academic leadership team. This supervisor will act as a mentor throughout the programme. Students normally have the opportunity to meet with their supervisor at least once every two weeks, averaged across the year. These meetings will serve to monitor academic progress as well as to discuss any academic issues or questions arising. 

In your second year, you will be allocated a main supervisor for your DPhil project and you will transition into their academic department where you will commence your research project. This supervisor is expected to be a member of the FoAI Supervision Pool. Students are encouraged to include additional co-supervisors from EIT. Additional co-supervision may also be arranged with Oxford academics beyond the supervisor pool.

Taught courses are generally assessed by a presentation in small groups on the material studied. Each of the two rotation projects will be assessed by researchers from the supervisor pool on the basis of a report written by the student. The year-long group project will be assessed by a joint presentation around the end of the first year.

All students will be initially admitted to the status of Probationer Research Student (PRS). Within a maximum of six terms, students will be expected to apply for transfer of status from Probationer Research Student to DPhil status.

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 their work continues to be on track. This will need to be completed within ten terms of admission.

Both milestones normally involve an interview with two assessors and therefore provide important experience for the final oral examination. Students will be expected to submit a thesis at four years from the date of admission.

The final thesis is normally submitted for examination during the fourth year and is followed by the viva examination. The final award for Oxford based students will be a DPhil awarded by the University of Oxford.

To be successfully awarded a DPhil you will need to defend your thesis orally (viva voce) in front of two appointed examiners.

This is a new course and there are no alumni yet. The CDT is dedicated to providing the organisation, environment and personnel required to develop a new generation of data scientists equipped to for a wide range of career paths in academia, research and industry.

This course is led by the MPLS Doctoral Training Centre

• Course page on the CDT's website
• Funding information from the CDT
• Academic and research staff at the CDT
• MPLS Doctoral Training Centre website
• Mathematical, Physical and Life Sciences
• Contact details

*One-year of applications (for entry in 2025-26) 

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

Entrance is highly competitive and most successful applicants have a first-class degree or the equivalent. 

A previous master's degree is not required, but it is expected that most applicants with a ‘physical science’ background will have completed a four-year integrated master's course. The undergraduate degree requirement may be alternatively demonstrated by strong performance in a relevant master's degree. 

For applicants with a bachelor's degree from the USA, the minimum overall GPA that is normally required to meet the undergraduate-level requirement is 3.6 out of 4.0. However, most successful applicants are expected to have a GPA of 3.8 or above.

GRE General Test scores

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

Other qualifications, evidence of excellence and relevant experience

Professional experience, especially research experience in artificial intelligence, is valuable and will be taken into consideration as a substitute for an academic qualification. Applicants must be able to demonstrate, in their statement of purpose and/or CV section of the application form, that they are highly numerate and capable of graduate level research in mathematics and computer science to complete the course. Prior research experience that has resulted in journal or conference publications should be included if present.

The department is looking for applicants whose research interests are aligned with its mission and whose academic and professional experience to date meet the following specification:

• Delivered an outstanding performance in an undergraduate or postgraduate degree in a subject that involves the extensive study of relevant graduate-level mathematics: We are looking for individuals with an exceptional academic track record who can demonstrate a high level of performance in a subject of study that involves graduate-level mathematical study. This would normally involve successfully completing undergraduate and possibly further postgraduate study in subjects such as mathematics, statistics, engineering, computer science or physics.
• Have demonstrable experience of artificial intelligence research: We are looking for individuals to provide evidence of their interest and commitment to artificial intelligence research commensurate with their career stage. This could involve research projects as part of their formal academic studies, internships or professional work experience.
• Possess a clear motivation for the study of fundamentals of artificial intelligence: We want to understand how your career record and experiences to date have led to an interest in the further study of the fundamentals of AI. We want to see a compelling vision for how this CDT will support your future aspirations.
• Demonstrates an understanding of how the fundamentals of artificial intelligence links to real world impact: While not all FOAI CDT students will have a deep interest in applications of AI and may prefer to focus on theoretical studies only, we want all students to have an appreciation of the link between fundamental AI research and how those outputs can be used in applications. If you are interested in applications, tell us how your interests align with the EIT themes and scientific programmes. If not, tell us how you think your theory and methods work could have a range of uses.
• Provides indicators of emerging leadership capabilities: We are seeking individuals who can demonstrate the potential to not only undertake great doctoral research but may have the ability to become leaders in their field and beyond. The CDT will develop these skills during the programme but we want candidates who can give examples where they have demonstrated strong communication skills, the ability to engage and work with others, or taken the initiative and responsibility. Evidence could be in the context of academic or professional work but can also be shown through personal interests, sports and hobbies (e.g. charity work).

Interviews are normally held as part of the admissions process and are expected to be held in February or March.

Interviews will be held online, normally with at least two interviewers from the supervision pool and one from EIT.

Prior to interview you may be required to prepare a short video highlighting your interest in a topic related to EIT and the CDT.

In some cases, you may be invited back for a second interview.

Interviews will focus on technical aspects, specifically your foundational mathematical, statistical and computational skills, your appetite to engage with cohort activities, and your commitment to innovation to solve global problems through technological advancements.

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

The following colleges accept students on this course:

• Jesus College
• Kellogg College
• Linacre College
• Reuben College
• Wolfson College

You do not need to contact anyone at the University prior to submitting your application. However, if you do have questions, informal enquiries should be made to the EIT CDT in Fundamentals of AI administrator in the first instance (See Contact details).

You are not expected to contact academic members of staff before applying and you will not be required to name any potential supervisors on your application form. 

It is not necessary for you to identify a potential supervisor in your application.

Your statement should be written in English and should focus on your motivation, research interests and career ambitions, rather than on other personal achievements, interests and aspirations.

You must structure your statement into the six sections listed below. Please ensure that the word counts are clearly displayed. The section headings will not count towards the word count. Please be as specific and detailed as possible in your answers. 

1. Give an overview of your academic education and professional career to date and how it led to an interest in conducting research in the fundamentals of AI (250 words)
2. Describe a research project you have undertaken that involved the development of AI theory or methods. What was the problem you addressed? What was your contribution to the development of AI? Explain why an existing, off-the-shelf solution could not be used. (Up to 400 words)
3. Describe a situation where you once made a programming error. How did you discover the error? How did you trace the source of the error? And how was it resolved? (Up to 250 words)
4. What makes the FoAI CDT more appropriate to you than other options for doctoral study? (Up to 150 words)
5. How could your interests support one or more of the EIT themes or scientific programmes (Up to 150 words)
6. If you are invited to interview, you will be asked to undertake some technical exercises. In order to help determine appropriate questions for you, please indicate one current primary area of interest from the following: (i) Foundations, (ii) Applied, or (iii) Systems. More information about these areas of interest can be found on the Research Areas section of this page. You response will be used as a guide and will not commit you to a particular area of research if you are offered a place on the course. 

Please note that if if these instructions are not followed, your application will not be considered as complete and may not be considered.

You may also explain any special circumstances relating to any element of your application that you wish to bring to the attention of the assessors in your statement. 

Your statement will be assessed for:

• your reasons for applying
• evidence of understanding 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 and ability to absorb new ideas often presented abstractly, at a rapid pace
• your willingness to engage with fundamental AI projects with potential to impact across a range of humane themes associated with the Ellison Institute of Technology (EIT)

Email: [email protected]