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University of Oxford: Fundamentals of AI (EIT CDT)

Institution University of Oxford
Department Medical Sciences Doctoral Training Centre
Web http://www.ox.ac.uk/study
Email graduate.admissions@admin.ox.ac.uk
Telephone +44 (0)1865 270059
Study type Research

Summary

The information provided on this page was correct at the time of publication (November 2025). For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas.

Fundamentals of AI (EIT CDT) is a research-based DPhil course focused on foundational AI, machine learning, and computational statistics. Students will help shape the future of AI and Machine Learning with a view to real-world impact.

The Centre for Doctoral Training's (CDT) aim is to develop AI technologies with the potential to drive transformative impact across key global challenges aligned with the missions of Ellison Institute of Technology (EIT).

The course will provide you with training in both cutting-edge AI research methodologies and the development of business and transferable skills. You will work with leading academics at the University of Oxford and will have the opportunity to work closely with project teams at EIT with access to both university and EIT facilities. You will undertake a significant, challenging and original research project, leading to the award of a DPhil.

While there can be many definitions of the fundamentals of artificial intelligence (FoAI), within the FoAI CDT, it is defined in three areas that allows a modern, inclusive and diverse interpretation of FoAI.

  • Theory and Foundations: Researchers in this area focus on the foundational mathematical, statistical, and computational principles that underpin AI. This includes research in topics such as learning theory, optimisation, stochastic analysis, complexity theory and formal methods. The aim is to create formal frameworks for the analysis of AI algorithms and systems in order to gain insight into properties, understand behaviours and to develop improved algorithms that could have widespread general use in the field.

  • Applied Fundamentals: At the FoAI CDT, researchers maybe interested in particular applications of AI relevant to EIT’s Humane Themes and Scientific Programmes. Researchers in this area will examine how scientific challenges and the properties of real-world data can guide the reformulation of existing AI algorithms or the design of new algorithms entirely. Topics in this area include physical and process modelling, how to handle missing data, multimodal data integration, decision support, etc.

  • Fundamentals of AI Systems and Engineering: In recent years, there has been an unprecedented emergence of large and complex AI systems, such as Large Language Models. Researchers in this area are interested in the formal frameworks for characterising the design and development of such systems and using these to further understand the properties and behaviours of such systems. They may also be interested in the security, scalability and physical resource requirements of such systems.

During the first year of the course you will take a number of taught courses.

The CDT directors will 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.

You will undertake two 10-week exploratory projects usually with different supervisors. Towards the end of the first year, you 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 different.

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.

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

Doctor of Philosophy - PhD

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