Various Subject Areas: Fully Funded UKRI CDT Artificial Intelligence, Machine Learning and Advance Computing (AIMLAC) PhD Scholarships 2023
Closing date: 10 February 2023
Funding provider: UK Research and Innovation (UKRI)
Subject areas: Biological and Health Sciences; Mathematics and Computer Science; Physics and Astronomy
Project start date: 1 October 2023 (Enrolment open from mid-September)
Aligned programme of study: Various PhD (Physics, Medical and Healthcare Studies, Computer Science)
Mode of study: Full-time only
This is a competitive scholarship scheme and three fully funded PhD scholarships are available at Swansea University.
Artificial Intelligence, Machine Learning and Advance Computing (AIMLAC) aims at forming the next generation of AI innovators across a broad range of STEMM disciplines. The CDT provides advanced multi-disciplinary training in an inclusive, caring and open environment that nurture each individual student to achieve their full potential. Applications are encouraged from candidates from a diverse background that can positively contribute to the future of our society.
The UK Research and Innovation (UKRI) fully-funded scholarships cover the full cost of tuition fees, a UKRI standard stipend of £17,668 per annum and additional funding for training, research and conference expenses.
The scholarships are open to UK and international candidates.
Its partner institutions are Swansea University (lead institution), Aberystwyth University, Bangor University, University of Bristol and Cardiff University.
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. Training will be delivered via cohort activities across the partner institutions.
Positions are funded for 4 years, including 6-month placements with the external partners.
AIMLAC CDT Project titles:
- RS191 - AIMLAC1 - Using Machine Learning to understand Lattice QCD Data (Physics)
- RS192 - AIMLAC2 - Optimising Attack-Defence Trees using Evolutionary Computing (Computer Science)
- RS193 - AIMLAC3 - Tests of the dark sector with gravitational waves (Physics)
- RS194 - AIMLAC4 - Data Lab Cymru (Medicine)
- RS195 - AIMLAC5 - AI based approaches multi-dimensional functional genomics in cancer patients (Medicine)
- RS196 - AIMLAC6 - Protein Structure Prediction via Deep Learning Protein Structure Prediction via Deep Learning (Computer Science and Biomedical Science)
- RS197 - AIMLAC7 - Development of a plasma lens for Laser hybrid Accelerator for Radiobiological Applications with an advanced computational approach (Physic and Medical Physics)
- RS275 - AIMLAC8 (FUNDING CONFIRMED) - Application of Machine Learning for non-destructive beam profile measurements at CERN’s Large Hadron Collider (LHC) (Physics)
Description of research projects and more information can be found at the UKRI CDT in Artificial Intelligence, Machine Learning & Advanced Computing (AIMLAC) website.
Please click on the link for the project you are interested and complete the APPLY online form. Please quote the project code (e.g. RS191 - AIMLAC1) for queries and within the application. If you wish to apply for more than one AIMLAC project, please complete a separate application for each project.
Please see individual project adverts for more information on eligibility.
This scholarship is open to candidates of any nationality.
NB: If you are holding a non-UK degree, please see Swansea University degree comparisons to find out if you meet the eligibility.
If you have any questions regarding your academic or fee eligibility based on the above, please email firstname.lastname@example.org with the web-link to the scholarship(s) you are interested in.
The scholarships cover the full cost of tuition fees and an annual stipend of £17,668.
Additional funds will be available for research expenses.
How to Apply
To apply, please visit the individual scholarship advert page.
For enquiries, please contact Roz Toft (email@example.com).