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Field
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quantum chemistry (QM), machine learning (ML), and high-throughput experimentation (HTE). The objective is to develop a data-driven framework that enhances the efficiency and effectiveness of catalyst
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oxides, hydroxides and hydrides using a combination of solid-state density-functional theory (DFT) and machine-learning force fields (MLFFs). DFT methods will be used to study materials of interest
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techniques. This research proposes a novel framework that integrates Machine Learning (ML) for structural health monitoring (SHM) and design optimization of CFDST wind turbine towers. The study will focus
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applications such as energy storage, solar, and carbon capture. The project will explore methods beyond traditional density-functional theory (DFT), leveraging cutting-edge techniques such in machine learning
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Supervisors: Prof. Gabriele Sosso, Dr Lukasz Figiel, Prof. James Kermode Project Partner: AWE-NST This project utilises advancing machine learning techniques for simulating gas transport in
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optimization of batteries against the swelling phenomenon. This project aims at developing scientific machine learning approaches based on the Bayesian paradigm and electrochemical-thermomechanical models in
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The University of Exeter’s Department of Engineering is inviting applications for a PhD studentship funded by the Faculty of Environment, Science and Economy to commence on 1 June 2025 or as soon as
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. Alternative approaches are graph-based molecule reaction space sampling and generative machine learning as they provide a path to new synthetic data that can form the basis for a large-scale database of
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Project title: Privacy/Security Risks in Machine/Federated Learning systems Supervisory Team: Dr Han Wu Project description: In the wake of growing data privacy concerns and the enactment
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PhD studentship: Machine Learning-driven Fusion of Drone-mounted Ground-penetrating Radar and Smart Meter Data for Advanced Leak Detection in Water Networks Award Summary 100% fees covered, and a