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supporting the Net Zero 2050 target. This PhD project will develop an AI-enabled framework that optimizes wind turbine control and predictive maintenance. Using Deep Reinforcement Learning (DRL), the system
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The primary objective of this project is to establish the evidence base on professional cycling road ‘racing’ trends and the critical tactical moments that determine how races are won. This evidence
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of Computational Solid Dynamics Building upon very recent work made by the supervisory team, this FULLY FUNDED 4-year PhD project will investigate challenging aspects such as: (1) exploration of a truly “n” multi
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development and refinement accordingly. We are looking for a highly organised, driven, and dynamic individual who is a team worker, has a positive outlook, and is adaptable and flexible in their working methods
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Description: This PhD project offers an exciting opportunity to co-develop and pilot a systems-based, interdisciplinary framework for circularity assessment in the water sector. Working closely with Anglian
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filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
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. Applications may close early if the position is filled before this date. Please note that any offer of funding will be conditional on securing a place as a PhD student. Candidates will need to apply separately
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is characterised by complex and highly dynamic turbulent flows that define the performance and design of renewable energy systems and their infrastructure. This PhD project aims to enhance
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to Research Associate (Grade 7) upon the award of PhD. If a PhD is not held, the appointment will be made at Research Assistant level. Scientific enquiries apart from the formal application should be directed
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Cranfield University invites applications for a PhD funded by Thames Water through the Ofwat Innovation Fund. The studentship covers full Home tuition fees plus a tax free stipend of £24,000 per