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UKRI CDT in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC)

The AIMLAC Centre for Doctoral Training provides 4-year, fully funded PhD opportunities across the broad areas of particle physics and astronomy, biological and health, and mathematical and computer sciences.

UKRI CDT in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC)

Training in AI, high-performance computing (HPC) and high-performance data analytics (HPDA) plays an essential role, as does engagement with external partners, which include large international companies, locally based start-ups and SMEs, and government and Research Council partners.

The CDT is built upon longstanding research and training collaborations between the universities of Aberystwyth, Bangor, Bristol, Cardiff and Swansea. In addition, Supercomputing Wales and the University Computing Academies provide bespoke support via Research Software Engineers and access to HPC facilities in a coordinated fashion. We work closely together with the STFC CDT on Data-Intensive Science.


Key research themes:

  • Data from large science facilities (particle physics, astronomy, cosmology)
  • Biological, health and clinical sciences (medical imaging, electronic health records, bioinformatics)
  • Novel mathematical, physical, and computer science approaches (data, hardware, software, algorithms)


The programme consists of a substantial training component in the first year, including cohort-based training in AI and computational methods, to establish a common base.

Engagement with our external partners is embedded throughout and includes a short-term placement in Year 2 and a further 6-month placement across Years 2/3.

Transferable skills training is delivered via residential meetings, and at our annual CDT conference. More details can be found on the Training and Events pages.


Fully-funded PhD positions are available for students with a strong interest and aptitude in computational science and in one of our research themes. Positions are funded for 4 years, including the placements with the external partners.

Considerable funding is available for training, workshop and conference support, as well as for a laptop and other computational resources.

Entry requirements

Open to EU, International (non-EU) and UK (home) students.

The typical academic requirement is a minimum of a 2:1 undergraduate degree in biological and health sciences; mathematics and computer science; physics and astronomy or a relevant discipline.

Candidates should be interested in AI and big data challenges, and in (at least) one of the three research themes. You should have an aptitude and ability in computational thinking and methods (as evidenced by a degree in physics and astronomy, medical science, computer science, or mathematics, for instance) including the ability to write software (or willingness to learn it).

IELTS – If applicable, a minimum overall IELTS score of 6.5 with no less than 6.0 in any individual component is required.


Partner institutions

All AIMLAC students will be based at one of the core AIMLAC institutions, in partnership with Supercomputing Wales.

  • Cardiff University
  • Aberystwyth University
  • Bangor University
  • Swansea University
  • University of Bristol


Our 28 partners are from research, industry, policy or third sector organisations will provide expertise in developing studentships, supervision, training, impact advisers, and internship opportunities:

  • Supercomputing Wales
  • Agxi
  • Airbus
  • Amplyfi
  • Atos
  • Dell
  • DiRAC
  • [dstl]
  • EDF
  • GCHQ
  • Heilbronn Institute for Mathematical Research
  • IBM
  • Intel
  • Microsoft
  • Mobileum
  • NHS Wales
  • Nvidia
  • Oracle
  • Oxford e-research Centre
  • Quantum Advisory
  • The Quant Foundry
  • QinetiQ
  • Stanley Black & Decker
  • UKRI Science and Technology Facilities Council
  • TWI
  • we predict
  • Welsh Water

Find out more

Please visit our CDT website for more details about the centre and how to apply.

For further information about the CDT studentships at Cardiff University please contact Professor Stephen Fairhurst.