UKRI CDT In Foundational AI


Programme structure and timeframes


There is no MRes Year: in our CDT we encourage students to jump straight into research with training and taught modules throughout the four years.

In year 1 students will receive basic disciplinary and interdisciplinary research training.  They audit up to five taught modules drawn from existing Masters programmes in Computer Science.  They will also complete a research project written up as a scientific article, presented orally, and translated into a document suitable for public consumption.  This research will form part of their PhD, the remainder of which will completed in years 2-4.

In year 2 the training focuses on the consolidation of disciplinary knowledge, research and transferable skills.  Students will take short courses and training in: responsible research and innovation and have the option to take courses in technical argument and communication, technical writing, teamwork and scientific and technical project management. 

In year 3 students will have the option of completing additional elective modules.  They will have the option to take training in entrepreneurship, scientific peer review, intellectual property, open access publication, licenses and policies, open data and open source. 

In year 4 students will be expected to mostly focus on the completion of their thesis and producing peer-reviewed papers, and will also receive extended training in preparation for a research career or as an entrepreneur.

In addition to the above, over the duration of the programme, all students will be expected to complete CALT training for teaching assistants, attend seminar series, and participate in an CDT annual event.

Available suggested modules

  • Reinforcement Learning (COMP0089)
  •  Advanced Topics in Machine Learning (COMP0083)
  •  Approximate Inference and Learning in Probabilistic Models (COMP0085)
  • Graphical Models (COMP0080)
  • Information Retrieval and Data Mining (COMP0084)
  • Introduction to Deep Learning (COMP0090)
  • Introduction to Machine Learning (COMP0088)
  • Inverse Problems in Imaging (COMP0114)
  • Machine Vision (COMP0137)
  • Probabilistic and Unsupervised Learning (COMP0086)
  • Supervised Learning (COMP0078)
  • Multi-agent Artificial Intelligence (COMP0124)

Outside of optional academic modules, we also offer courses with the Alan Turing Institute and require that our students attend mandatory Responsible Research and Innovation courses.

These are some examples, but students are encouraged to explore where gaps in their knowledge are with their supervisor and we will help them locate appropriate courses and training to fill those gaps. Please be aware that there may be restrictions on the choice of modules to audit, not all choices may be possible or approved depending on availability.

Project Partners & Entrepreneurship

As part of the CDT, students will also be trained to become Entrepreneurs in their own right, should they wish to after your PhD. We will offer a number of training events and opportunities over the course of the four years to accomodate that, including the possibility of internships where appropriate.