EPSRC logo

Details of Grant 

EPSRC Reference: EP/S023992/1
Title: UKRI Centre for Doctoral Training in Artificial Intelligence, Machine Learning and Advanced Computing
Principal Investigator: Aarts, Professor G
Other Investigators:
Bremer, Professor MN Shen, Professor Q Zhou, Dr S
Fairhurst, Professor S Flaecher, Dr HU Whitaker, Professor R
Roberts, Professor JC Paizs, Professor B Zwiggelaar, Professor R
Lucini, Professor B
Researcher Co-Investigators:
Project Partners:
Amplyfi Atos UK&I Defence Science & Tech Lab DSTL
Evolved Intelligence GCHQ IBM
Microsoft MP Capital Nightingale-EOS
QinetiQ Quantum Advisory STFC Laboratories (Grouped)
TWI Ltd University of Leicester We Predict Ltd
Welsh Water (Dwr Cymru)
Department: College of Science
Organisation: Swansea University
Scheme: Centre for Doctoral Training
Starts: 01 April 2019 Ends: 30 September 2027 Value (£): 5,426,912
EPSRC Research Topic Classifications:
Accelerator R&D Artificial Intelligence
Astron. & Space Sci. Technol. B Physics/Flavour Physics
Cosmology Data Handling & Storage
Extra-Galactic Astron.&Cosmol. Extra-Galactic Astron.&Cosmol.
Lattice QCD
EPSRC Industrial Sector Classifications:
Water Manufacturing
Financial Services Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
07 Nov 2018 UKRI Centres for Doctoral Training AI Interview Panel W – November 2018 Announced
Summary on Grant Application Form
We live in a society dominated by information. The collection of data is an ongoing and continuous process, covering all aspects of life, and the amount of data available in recent years has exploded. In order to make sense of this data, utilise it, gain insights and draw conclusions, new computational methods to analyse and infer have been developed. This is often described by the general term "artificial intelligence" (AI), which includes "machine learning" or "deep learning", which rely on the processing of information by computers to extract nontrivial information, without providing explicit models. Highly visible are developments driven by social media, as this affects every person in a very explicit manner. However, AI is widely adopted across the industrial sectors and hence underpins a successful growth of the UK's economy. Moreover, also in academic research AI has become a toolset used across the disciplines, beyond the traditional realms of computer and data science. Research in science, health and engineering relies on AI to support a wide range of activities, from the discovery of the Higgs boson and gravitational waves via the detection of breast cancer and diabetic retinopathy to autonomous decision- making and human-machine interaction.

In order to sustain the industrial growth, it is necessary to train the next generation of highly-skilled AI users and researchers. In this Centre for Doctoral Training, we deliver a training programme for doctoral researchers covering a broad range of scientific and medical topics, and with external partners engaged at every level, from large international companies via government agencies to SMEs and start-ups. AI relies on computing and with data sets growing ever larger, the use of advanced computing skills, such as optimisation, parallelisation and scalability, becomes a necessity for the bigger tasks. For that reason the CDT has joined forces with Supercomputing Wales (SCW), a new £15 million national supercomuting programme of investment, part-funded by the European Regional Development Fund. The CDT will connect researchers working at Swansea, Aberystwyth, Bangor, Cardiff and Bristol universities with regional and national industrial partners and with SCW. Our CDT is therefore ideally placed to link AI and high-performance computing in a coordinated fashion.

The academic foundation of our training programme is built on research excellence. We focus on three broad multi- disciplinary scientific, medical and computational areas, namely

- data from large science facilities, such as the Large Hadron Collider, the Square Kilometre Array and the Laser Interferometer Gravitational-Wave Observatory;

- biological, health and clinical sciences, including access to electronic health records, maintained in the Secured Anonymised Information Linkage databank;

- novel mathematical, physical and computer science approaches, driving future developments in e.g. visualisation, collective intelligence and quantum machine learning.

Our researchers will therefore be part of cutting-edge global science activities, be able to modernise public health and determine the future landscape of AI.

We recognise that AI is a multidisciplinary activity, which extends far beyond single disciplines or institutions. Training and engagement will hence take place across the universities and industrial partners, which will stimulate interaction. Ideally, a doctoral researcher should be able to apply their skills on a research topic in, say, health informatics, particle physics or deep learning, and be able to contribute equally.

To ensure our training is aligned with the demands from industry, the CDT's industrial partners will co-create the training programme, provide input in research problems and highlight industrial challenges. As a result our researchers will grow into flexible and creative individuals, who will be fluent in AI skills and well-placed for both industry and academia.
Key Findings
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Potential use in non-academic contexts
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Impacts
Description This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Summary
Date Materialised
Sectors submitted by the Researcher
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Project URL:  
Further Information:  
Organisation Website: http://www.swan.ac.uk