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difficult to deploy outside large data centres. This PhD project focuses on developing resource-efficient computer vision methods that maintain strong performance while dramatically reducing computation
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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of the following: numerical methods, high-performance computing (HPC), Computational Fluid Dynamics (CFD), applied mathematics, physics, engineering or subsurface flow modelling. Enthusiasm
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engineering, physics and applied mathematics. You should have experience in one or more of the following: numerical methods, high-performance computing (HPC), Computational Fluid Dynamics (CFD), applied
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The Surrey team has recently demonstrated the performance of new perovskite scintillator materials which combine a high scintillation light yield, high material density, good optical transparency
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This studentship aims to develop valid and high-fidelity immersive simulations to support the transfer of performance to competitive sporting environments. Immersive simulation is widely used in
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Storage Options and Heat Decarbonisation Pathways Task 3.1: This task will assess electrolyser performance under variable PV and heat demand using hourly data, optimizing sizing and dispatch with HOMER Pro
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Project Supervisor - Dr Stefano Landini The rapid evolution of electric vehicles (EVs), data centers, and high-performance energy storage is driving the need for battery systems that manage heat
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1. Project overview Electrochemical sensing plays a central role in next-generation health technologies, enabling real-time monitoring of physiological environments with high sensitivity and low
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chemistry modelling techniques scientific machine learning high-performance computing molecular design, generative AI, database handling and analysis collaborative, project management, presentation and