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element modeling, computational fluid dynamics). Knowledge of heat and mass transport processes in heat-sensitive materials and process optimization. Experience in supply chains and hygrothermal
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overcomes the geographic limitations of conventional systems, enabling global scalability and accessibility. Using advanced computational fluid dynamics (CFD) approaches, the project is aimed at advancing
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prediction, signal tracking, fluid dynamics, and space exploration. Advancing Signal Modelling with Physics-Informed Neural Networks This project aims to develop Physics Informed Neural Networks (PINNs
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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
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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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: 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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Supervisors: Dr Emmanouil Kakouris, Prof. James Kermode, Project Partner AWE-NST Find out more: https://warwick.ac.uk/fac/sci/hetsys/themes/projects2025 High-rate ductile fracture, particularly in
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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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Supervisors: Dr Raj Pandya, Prof. Nicholas Hine, Prof. Reinhard Maurer While we as humans are used to seconds and hours, electrons and atoms in materials move a whole lot faster around a million
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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