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Field
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We are seeking an outstanding candidate for a PhD fellowship in the field of computational fluid and solid mechanics. The fellowship will start on September 1st, 2025, or as soon as possible after
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research team. Good knowledge and experience in heat and mass transfer is essential and proficiency in the use of Computational Fluid Dynamics will be considered an advantage. The student will benefit from
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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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Establishment). Recent work by the group (leading to REF 4* rated outputs and several Keynotes) has contributed to bridging the gap between Computational Solid and Fluid Dynamics, with a unified computational
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This is a self-funded opportunity relying on Computational Fluid Dynamics (CFD) and wind tunnel testing to further the design of porous airfoils with superior aerodynamic efficiency. Building
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systems, enabling global scalability and accessibility. Using advanced computational fluid dynamics (CFD) approaches, the project is aimed at advancing modelling capabilities for the prediction of energy
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, combustion, and process optimisation. The project is focussed on the development of novel interface capturing Computational Fluid Dynamics methods for simulating boiling in Nuclear Thermal Hydraulics
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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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sound background in geophysical fluid dynamics, experience in numerical ocean or atmospheric modelling, and experience with numerical data analysis. Good scientific presentation, writing, and
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applied physics other related disciplines. Demonstrated knowledge in at least one of the following areas: porous media flow computational fluid dynamics (CFD) pore-network modelling lattice Boltzmann method