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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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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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advanced operational strategies, such as model-predictive control, tailored to dynamic prosumer energy demand. Foster collaboration: Work closely with industrial and research partners, including CENAERO
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Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling
the synthesis and assembly of these cell surface proteins. A major and complex means by which bacterial gene expression is regulated to control surface protein production depends on RNA-RNA interactions
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14th September 2025 Languages English English English The Department of Electronic Systems has a vacancy for a PhD Candidate in Wind Turbine Noise Prediction in the Norwegian Context Apply
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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