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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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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
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programmed in advance. If anything changes, it may fail. This project explores how to build more adaptable systems using vision-language-action (VLA ) models. These combine computer vision (to see), natural
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will include advanced 2Dm synthesis, acoustic manipulation, and device fabrication in Exeter’s and Bath’s facilities, alongside simulation (thermal energy) and characterisation methods (Raman, AFM, SEM
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systems using vision-language-action (VLA ) models. These combine computer vision (to see), natural language understanding (to interpret instructions), and action generation (to respond), enabling robots
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allowance of £20,780 (2025/26 UKRI rate). Additional project costs will also be provided. Overview The state-of-the-art in computer-aided drug design is physics-based modelling, in which candidate drugs and
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, finite element simulations of vibration propagation, and AI-based signal analysis to establish a physics-informed understanding of the relationship between structural behaviour and fall detection accuracy
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proposal is not required, instead upload a copy of the project name as a word file. For queries regarding the application process, please contact pgr.sst.enquire@citystgeorges.ac.uk City, University
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allowed computational fluid dynamics (CFD) to flourish, becoming an indispensable for many industries. Simulating the full Navier-Stokes equations is computationally prohibitive for most applications, so