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Field
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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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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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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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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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, therapy resistance driven by tumour heterogeneity continue to limit the long-term success of current treatments. The candidate will analyse patient biopsies, cerebrospinal fluid (CSF), blood samples
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hypothesis of the proposed research is by use of intelligent and integrated control of the input power electronics, fluid handling, and thermal control in a holistic approach, current efficiency and lifespan
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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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Current modelling and simulations require either generic assumptions to be made for fluid dynamic based modelling leading to inaccuracies between modelled and experimental data or, intense
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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