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: A qualifying university master’s degree in physics, engineering, meteorology, or a comparable field. Knowledge of fluid dynamics or nonlinear systems Experience in programming with Matlab or Python
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technological solutions for the recycling of waste heat in specific food industry settings, using computational fluid dynamics modelling, lab experiments and field work To disseminate finding in high impact
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. This project seeks to enhance the phase-field method, enabling more accurate predictions of fracture under dynamic conditions. State-of-the-art computational techniques combined with insights from advanced
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Supervisors: Prof. Gabriele Sosso, Dr Lukasz Figiel, Prof. James Kermode Project Partner: AWE-NST This project utilises advancing machine learning techniques for simulating gas transport in
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. This project will develop and apply new computational/analytical tools to guide XFEL experiments for specifically tracking lattice fluctuations and ion dynamics in energy materials (batteries). The project will
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I offer projects broadly related to supernova explosions and the final stages in the lives of massive stars. Specific topics of interest include fluid dynamics processes in stellar explosions and