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Field
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equations into AI-based models to solve fluid sensing problems in a robust and efficient manner. Your role may include developing new optimization techniques, coding new algorithms, creating new mathematical
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-material capability with a suitable closure model; (2) improved strategy for interface tracking/capturing; (3) very high-speed scenarios with use of nonlinear Riemann-solvers. If time allows exploratory 3D
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(developed by B.J. Evans, O. Hassan and K. Morgan). This solver directly solves the Boltzmann-BGK model equation for the velocity distribution function, which is a fundamental quantity in rarefied gas
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nature of electroweak symmetry breaking and mass generation in the standard model. We developed state of the art (open source) software working on GPU- and CPU-based supercomputing architectures, and
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, and 2) to develop a physical model of cell shape dynamics during EMT. You should hold a PhD (or about to be awarded a PhD) in Biophysics or a related field and have extensive experience with cell and
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expertise and facilities in electrochemistry, materials chemistry, advanced characterisation techniques (including a variety of spectroscopy, microscopy,) modelling and battery and fuel cell construction and
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, which are essential for safe operation in these challenging aerospace environments. You will develop robust, physics-based models to analyse failure, with a focus on understanding mechanical and
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of the particle fuel, crack initiation/propagation and failure mechanisms in relation to test temperature. Finite element (FE) modelling using FE tools such as Abaqus, (or) Ansys, (or) COMSOL is optional
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. A non-deterministic AI machine learning model for the identical task would not offer this demonstrability or, critically, the repeatability of classical algorithm-based systems. Furthermore, there is
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hardware and data handling toolchains. The research will also involve modelling, analysis and characterisation of the system and components. The PhD will include a placement of minimum 3 months with Team