UKRI Centre for Doctoral Training in Artificial Intelligence, Machine Learning and Advanced Computing

Lead Research Organisation: Bangor University
Department Name: Sch of Computer Science & Electronic Eng


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 terms "artificial intelligence" (AI), "machine learning" or "deep learning", which relies 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 industrial partners engaged at every level. 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 supercomputing programme of investment, part-funded by the European Regional Development
Fund through the Welsh Government. The CDT will connect researchers working at universities in Wales (Swansea,
Cardiff, Aberystwyth and Bangor) and Bristol 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 across Wales and the South
The academic foundation of our training programme is build 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
computer vision, 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.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023992/1 31/03/2019 29/09/2027
2281471 Studentship EP/S023992/1 30/09/2019 30/03/2022 Vanessa Claire Cassidy