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hydrodynamic modelling remains largely unexplored, creating an opportunity to unite data-driven and physics-based approaches across diverse UK hydrological regimes. This project will develop a national
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). Overview This PhD will develop a data-driven framework to understand how climate-driven extreme weather affects electricity distribution networks and how targeted interventions can reduce
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allowance of £21,805 (2026-27 UKRI rates). Overview Artificial Intelligence(AI)-driven decision engines are increasingly embedded into cyber‑physical systems (CPS) such as autonomous vehicles, smart grids
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observations, wireline logs and seismic data to develop geological and petrophysical models that constrain reactive reservoir simulations. These models will improve predictions of mineral alteration
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Years Application Closing Date 15th October 2026 Sponsor British Heart Foundation ( https://www.bhf.org.uk/ ) Supervisors Dr Simon Bamforth https://www.ncl.ac.uk/medical-sciences/people/profile
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. The PhD candidate, funded by the UKHSA, will work with the IID3 study team and the UKHSA using study data. The aim is to understand how laboratory methods can affect the detection of pathogens and how
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). Minimum Data: ‘Research Stream’ and ‘Scholarship Category’ are required for Minimum data (see Section 3 of Appendix C – Guide to Applicants). Track Record: In addition to Publications, ‘Research Outputs
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agricultural systems, combining biodiversity surveys, animal movement data, landscape modelling and remote sensing to understand how trees within agricultural landscapes influence wildlife movement and
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alteration. The project is funded by Eni S.p.A through the UK Energy Futures ( https://ukenergyfutures.org ). research partnership, bringing together geoscientists, engineers, and social scientists to