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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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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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Predictive Control (MPC) algorithms, innovative coalition-formation techniques, and validate these through high-fidelity simulations. You will design, implement and validate innovative data-driven economic
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difficult for industry to predict and control product performance. This PhD project will tackle that challenge by applying advanced polymer characterisation techniques to better understand and quantify
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physical health conditions. A successful randomised controlled trial evaluated IPS for people with alcohol and drug dependence, and IPS services are now commissioned in 95% of local authority areas across
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AI approaches have recently been used to detect Alzheimer’s disease from CFPs among those with established disease (in case-control studies), the use of such approaches to predict disease (i.e., in
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application areas: the simulation of electromagnetic fields in high-speed electrical interconnects in the semiconductor industry; the prediction of the electromagnetic performance of communications devices
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candidate will contribute to feasibility studies on hybrid propulsion systems, the development of robust control architectures for autonomous docking, and predictive maintenance strategies using real-time
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of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
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recycle content crush alloys. The main objective of the project is to understand the deformation behaviour of the high recycle content crush alloys and the role of tramp elements in controlling the final