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-agent systems. Methodology: Integrate ToM models with Reinforcement Learning based frameworks for single-agent and multi-agent decision-making. Develop simulation environments capturing realistic human
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relevant discipline. Experience with modelling or simulation tools (e.g., MATLAB/Simulink, PSCAD, PowerFactory, or Python) is desirable.
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uncertainties make planning and decision-making more complex and difficult to optimise using conventional approaches. This project aims to develop advanced modelling and simulation frameworks to support decision
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aims to develop computational models to support the optimisation of plasma atomic layer deposition equipment. The PhD student will undertake numerical simulation, optimisation studies, and explore
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harvesting recent breakthroughs in Machine Learning (ML) and analytical modelling. Specifically, this project seeks to quantify key performance metrics and create powerful adaptive ML-driven management methods
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regulatory obligations. Hydraulic simulators are physically detailed but computationally slow and calibration-intensive, limiting large-scale scenario exploration and optimisation. Purely data-driven
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development, modelling and simulation, through to fabrication and advanced optical/electrical characterization—offering a unique opportunity to push the boundaries of photonic device engineering. This is a 3.5