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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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for modelling protein structures and protein-ligand (metabolite) interactions. Using multi-scale molecular models, we aim to predict the structural features of gibberellin (GA) transporters, a key phytohormone
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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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researchers addressing complementary topics and methodology such as Thermodynamic modelling of multi-component planetary degassing/ingassing, Molecular Dynamic simulations of silicate melts, Petrology