Programme

The EIT CDT in Fundamentals of AI based at the University of Oxford is part of a strategic collaboration between the University and the Ellison Institute of Technology. It will support up to 100 doctoral researchers to undertake foundational research in the underpinning theory and methods of Artificial Intelligence that have the potential to have a transformative impact on the field of artificial intelligence itself and across a range of humane themes associated with the EIT.

The programme will provide students with training in both cutting-edge AI research methodologies and the development of business and  transferable skills. Students will undertake a significant, challenging and original research project, leading to the award of a DPhil.

Key topics and themes will focus on fundamentals of artificial intelligence, computational statistics and machine learning. 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. 
The CDT directors will also meet with students individually during induction and throughout the first year to create personal development plans to help identify training which would be of particular benefit. 

2025-26 Modules

The specific content of the modules delivered is likely to evolve based on the needs of each cohort. These modules are indicative of the type of material students would be likely to study. 

  • Software Engineering Training This course introduces software engineering for 1st year DTC students. 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. 
  • Fundamentals of AI I: Foundational Principles This course covers a wide variety of machine learning methods, ranging from those that can be deeply understood and work very well under well-controlled conditions (Bayesian statistical methods like Gaussian processes), to general machine learning methods that are applied at large scale (neural processes and generative models). The general framework of decision theory will connect the problem formulations and provide students with the tools to develop strong empirical assessment of performance. This is crucial for understanding how the field evolves, as well as for the implications of deploying such solutions in practice. 
  • Fundamentals of AI II: Modern Statistical Concepts 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. 
  • Emerging Research and Skills 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’ve not considered before and trigger new ideas. 
  • Wider AI skills training these sessions will be looking at areas such as data management, high-performance computing, research publishing, ethics and regulation. 

 

Group Projects

  • Early on in term 1, we will put you into teams 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 with EIT early in your DPhil. These projects will be one of your main occupations during Term 2 of the first year. A little time will be set aside to continue working on these projects for the Term 3 and the summer alongside your individual projects.  

Rotation Projects

  • Students 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 students are encouraged work with additional co-supervisors from EIT. Students may also work with other co-supervisors at the university who are not in the supervision pool. During this time, students will be based in the home department of the mini-project supervisors.  
Towards the end of the first year, students will select a DPhil research project which may be a continuation of one of the short rotation projects, a topic from the group projects or something entirely different but we will guide you through that process. 
All projects (group, rotation & DPhil) will focus on underpinning theory and method development of Artificial Intelligence and machine learning that will have the potential to have a transformative impact across a range of themes associated with EIT.   

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.

MPLS Doctoral Training Centre Research and Professional Skills Training  

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.