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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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Applications are invited for a Doctoral Candidate in SHIELD (Strategies for Healing Implant-Associated Infections and Enhancing Longevity in Devices) Doctoral Network. The candidate would be working
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critical to ensuring the longevity and safety of fusion reactors. This PhD project focuses on developing an integrated framework that combines cutting-edge computational models, including Monte Carlo
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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
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at international conferences and build a professional network across academia and industry. Development of expertise in cutting-edge experimental techniques, computational modelling, and interdisciplinary
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Training Programme, those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs. including sessions on paper writing, networking and career development after
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access to bespoke courses developed for Engineering PGRs. including sessions on paper writing, networking and career development after the PhD. The Faculty has outstanding facilities and works in
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This PhD studentship is a part of Rolls-Royce sponsored research centre developing advanced control systems to enhance the performance and efficiency of new aerospace propulsion systems. We apply a
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the solution of governing PDEs. - Train machine learning models to predict lifetime and failure based on loading and environmental histories. The PhD student will have access to world-class computing facilities
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, organisational and policy context of the National Health Service. The PhD research will focus on how bottom-up networks are involved in promoting change. In recent years, numerous networks of clinicians