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programme aims to advance fundamental understanding of heat transfer and turbulence physics in wall-bounded flows through numerical simulations, data-driven modelling, and machine learning techniques. Key
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expected to contribute to other activities and tasks within the ENER-G project. The main outcome(s) of the PhD will be novel optimisation models and solution algorithms that will substantially advance
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of heat transfer and turbulence physics in wall-bounded flows through numerical simulations, data-driven modelling, and machine learning techniques. Key goals include optimising convective heat transfer
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to develop numerous collaborations and internships abroad, which will be an asset when it comes to obtaining a postdoc after the thesis. Experimental work in the laboratory will be a major part of this thesis