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About the project: Extreme Weather Storylines via Generative AI Supervisor: Dr Tobias Grafke, University of Warwick Generative machine learning techniques, as known for example from large language
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optimisation tools for aperiodic lattice metamaterials. The research will integrate physics-based modelling with machine learning to enable the efficient exploration of vast design spaces defined by
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. Project Overview The project focuses on developing and applying advanced CFD models for aeroengine oil systems. There will also be opportunities to integrate machine learning techniques for building lower
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AI hardware beyond traditional computing architectures. Gain a unique combination of skills in mathematics, machine learning, and photonics. Be part of a multidisciplinary research team spanning
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mathematics, machine learning, photonics, and clinical practices in vision. Be part of a multidisciplinary research team spanning science and engineering, psychology, and healthcare. Access state-of-the-art
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a mixture of computational, analytical and machine learning approaches to model the heat transfer to fuels and their physical and chemical behaviour, including changes in chemistry and physical
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This exciting opportunity is based within the Power Electronics and Machines Control Research Institute of the Faculty of Engineering at the University of Nottingham which conducts cutting edge
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the PhD to develop skills in areas such as programming, data analysis, machine learning and signal processing. This will provide the technical foundation required to work with large acoustic datasets and
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made significant progress in this direction by merging machine learning interatomic potentials (MLIPs) trained on density functional theory (DFT) data, and enhanced sampling techniques to reach the
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, machine-learned interatomic potentials, molecular dynamics, kinetic Monte Carlo modelling and comparison with experimental data from the Faraday Institution FAST programme. Faraday Institution PhD students