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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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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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of an external magnetic field into the phase-field model and to simulate the microstructural evolution for different cooling rates. - Phase-field modeling of the phase transition in the Fe-Ni alloy. The selected
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methodology such as Thermodynamic modelling of multi-component planetary degassing/ingassing, Molecular Dynamic simulations of silicate melts, Petrology of melting of exoplanetary mantles, and the partitioning
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prerequisite (i.e., familiarity with linux, bash, conda, python). Experience in molecular dynamics simulation, protein chemistry or phylogenetics would be major assets. An interest in developing wet lab skills
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researchers addressing complementary topics and methodology such as Thermodynamic modelling of multi-component planetary degassing/ingassing, Molecular Dynamic simulations of silicate melts, Petrology
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/ climate, risk engineering, data & AI (modelling, simulation, decision support), GIS and spatial analysis, analytical chemistry or toxicology, risk economics / management, law & governance, regulation