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Details of Grant 

EPSRC Reference: EP/Y030826/1
Title: UKRI AI Centre for Doctoral Training in Decision Making for Complex Systems
Principal Investigator: Alvarez Lopez, Dr M A
Other Investigators:
Handl, Professor JK Kaski, Professor S Mukherjee, Dr A
Sun, Dr M Jamnik, Professor M Allmendinger, Professor R
Ek, Dr CH Pan, Dr W Scaife, Professor AMM
Researcher Co-Investigators:
Project Partners:
Arca Blanca ArcelorMittal ARGANS Ltd (UK)
Arup Group Ltd Biopharm Services Limited City Football Group HQ
Cummins (Group) Dover Harbour Board (DHB) Finland Center for AI
Fujitsu Gendius Limited Henry Royce Institute
Honda IBM UK Ltd Monumo
Ofcom Siemens Slalom
Transport for Greater Manchester UK Atomic Energy Authority
Department: Computer Science
Organisation: University of Manchester, The
Scheme: Centre for Doctoral Training
Starts: 01 July 2024 Ends: 31 December 2032 Value (£): 7,785,861
EPSRC Research Topic Classifications:
Artificial Intelligence Astron. & Space Sci. Technol.
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
06 Sep 2023 UKRI CDTs in Artificial Intelligence 2023 expert panel Announced
20 Sep 2023 UKRI CDTs in Artificial Intelligence Interview Panel A Announced
Summary on Grant Application Form
This CDT will focus on challenging aspects of developing, deploying and assessing AI and data-driven solutions for semi or complete automation of real-world systems. Such systems usually involve several decision-making agents, including humans, and typically require coordinating hundreds to thousands of decisions. The CDT will specialise in training a new generation of AI researchers with the theoretical and practical skills to develop and test new machine learning models and approaches that can efficiently cope with uncertainty in complex systems. The CDT understands systems in a broader sense, including multi-agent systems in robotics, supply chain management and team sports, to systems in scientific domains such as astronomy and molecular biology.

A consortium between the University of Manchester and the University of Cambridge will lead the CDT. It will bring together researchers in machine learning with a strong track record in developing methodologies and applying these to real-world data and domain scientists with a strong track record of data-driven learning. The consortium has significant experience in the type of translational AI research that is at the core of the CDTs vision. The team is a balanced mix of AI experts, from Turing World-Leading Researcher Fellows to early-career rising stars, domain experts from selected fields of science, decision scientists, and a diverse set of companies. The priority area for the CDT will be Science and Research, starting with three fields of research, physics/astronomy, engineering biology and materials science -- and generalising the solutions to decision-making broadly in complex systems. We will train the students with the relevant knowledge and skills such that the CDT will also contribute to AI for increasing business productivity as a cross-cutting theme.

PhD projects will be co-created between AI specialists and domain experts. During their first year, students will receive foundational training in AI and ML, relevant training in the specific field of research related to the student's project, and research training through individual and group projects. From the second year and throughout their programme, students will have access to a wider training experience in entrepreneurship and public engagement. At the same time, students will engage in cohort training activities, including journal clubs and a yearly conference. Furthermore, students will benefit from the strong institutional support around Responsible Research and Innovation and Equality, Diversion and Inclusion, already in place at both Universities.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
Sectors submitted by the Researcher
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Project URL:  
Further Information:  
Organisation Website: http://www.man.ac.uk