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start date is October 2026. We recommend that you apply early as the advert may be removed before the deadline. This project focuses on developing advanced simulation techniques to optimise
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/AI: Apply data-driven methods to construct reduced-order models that bridge analytical theory & high-fidelity simulation data, enabling rapid drag prediction across surface parameter spaces
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Location: South Kensington campus About the role: Applications are invited for a fully funded fixed-term position at the Research Assistant/Associate (PostDoc) level in simulating the atmospheric
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on the adsorption mechanisms of these molecules on the metallic surfaces. In this PhD project we will use state-of-art molecular simulation methods [2,3] to clarify the adsorption and desorption mechanisms of various
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About the project: From Brittle to Ductile: Machine Learning 3D Fracture Simulations for Extreme Environments Supervisor: Prof, James Kermode, University of Warwick Develop cutting-edge machine
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) computational simulations to understand both the electronic states of enzyme intermediates and the transitions between them. As a joint studentship, around half of the project will be based in Manchester and half
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Fully-funded 4-year PhD Studentship (UK Home fee status): Numerical simulation of boiling flows for high heat flux fusion components Aim and Objectives This project aims to develop a high-fidelity
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process and equipment optimisation Application of artificial intelligence, surrogate modelling, and optimisation methods to accelerate exploration of RAM design and operating space. By coupling simulation
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them for specific tasks. The project will combine: Mathematical modelling of dynamical systems; Computational photonics simulations; Comparison with real physical systems (especially photonic systems
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will use a high-fidelity large eddy simulation (LES) code and scientific machine learning tools, such as real-time optimisers, in order to simulate wind farms exposed to various atmospheric inflows. Some