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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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Start date – 1st March 2027 (latest) Project Description Machining generates large quantities of swarf, with many components losing 60–90% of their material during the machining stage. This swarf
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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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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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, 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
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generative AI framework that utilizes machine learning predictions and quantum chemistry simulations to design stable, synthesizable, high-performance molecules. The framework will integrate multi-objective
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chemistry. Working with us means learning something new every day. You will be affiliated with the research project, with the working title Q4-BIO, where we open a new field of research around quantum tools
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machine learning and conventional optimisation techniques. 2. To design and optimise magnonic primitives for wave-based neuromorphic computing, including programmable devices enabling nonlinear activation
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existing models struggle to capture this complex, multiscale phenomenon efficiently. This project will develop a novel, physics-informed surrogate model using Bayesian machine learning to predict gas
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departments of this type in the country, with a particular strength in power engineering. The increasing use of liquid cooling in aerospace electrical machines introduces direct interaction between liquid