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Field
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-fidelity industrial simulation. Engineering applications include Formula 1, turbomachinery and nuclear fusion. These problems exhibit complex geometries and challenging time-varying physics which require
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good Master’s degree in Mathematics, Statistics, Computer Science, Engineering, Physics, with good programming, analytical problem-solving, writing and presentation skills. Please direct project specific
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hydrological systems. This project will develop a robust modelling framework to simulate future changes in water resources in North and East England, using a combination of physically-based hydrological
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to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/ For eligible successful applicants, the studentships comprises
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productivity and energy efficiency. Bioprocess modelling: Employ simulation, techno-economic analysis (TEA), and life-cycle assessment (LCA) to assess cost and GHG performance. Candidate’s Competencies and
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continuous operations at lab scale. In Situ Product Recovery (ISPR) evaluation: Test ISPR methods to boost productivity and energy efficiency. Bioprocess modelling: Employ simulation, techno-economic analysis
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Learning heat: Physics-Informed Fourier Neural Operators for High-Fidelity Thermal NDE Modern non-destructive evaluation (NDE) increasingly relies on AI models that can reason with physics, scale
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target feature extraction Experimental validation and assessment of real performance limits The algorithms we will derive will be theoretically designed, verified by simulation, and tested in the field
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to simulate and optimise the high-temperature chlorination process used by Krystal Technology Ltd, a UK-based leader in high-purity quartz production. This project will deliver the first comprehensive CFD-DEM
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Element Method (CFD-DEM) model to simulate and optimise the high-temperature chlorination process used by Krystal Technology Ltd, a UK-based leader in high-purity quartz production. This project will