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enhance system reliability and safety, aligning with the UK’s NetZero targets. Aim You will have the opportunity to build a high-fidelity process simulation and perform experimental validation to assess
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. Aim You will have the opportunity to build a high-fidelity process simulation and perform experimental validation to assess the structural performance of composite sleeves under operational conditions
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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gate drive implementations capable of maintaining reliable switching performance under cryogenic thermal conditions. This project will involve a substantial amount of experimental work using the high
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the “Dialling up Performance for on Demand Manufacturing” Programme Grant, which will place the student within an active and supportive team of 9 other PhD students, 15 postdoctoral researchers, 18 world-leading
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-funded doctoral training programme dedicated to academic research in space propulsion. R2T2 PhD programmes are already underway at nine UK universities, and the programme overall is centred on the Westcott
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solutions for maritime propulsion systems. The primary aim of the project is to design and implement a flexible, high-performance control strategy for an integrated power system consisting of an ammonia
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This PhD project aims to reduce—by approximately an order of magnitude—the time and computational effort required to evaluate point-defect energetics in technologically important mixed-anion
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the development of system software. Key questions include how LLMs can support programmers in writing complex logical code, generating high-quality tests, and optimizing performance. Moreover, when integrated with
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by the Ada Lovelace Centre and the University of Birmingham. This interdisciplinary project is ideal for candidates with a background in physics, materials science, chemistry, or computational science