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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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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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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 possible thereafter. For eligible students the studentship will cover Home tuition fees plus an...
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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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of dehydration using a low-power radio-frequency (RF) sensor. The research objectives include design optimization to improve wearability, robust data acquisition using machine learning and establishing correlation
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Open PhD position: Autonomous Bioactivity Searching Subject area: Drug Discovery, Laboratory Automation, Machine Learning Overview: This 42-month funded PhD studentship will contribute to cutting
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Open PhD position: Autonomous Bioactivity Searching Subject area: Drug Discovery, Laboratory Automation, Machine Learning Overview: This 42-month funded PhD studentship will contribute to cutting
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simulation regimes by harnessing and advancing the latest developments in AI Machine Learning. This studentship is a continuation of prior work that is looking at using new cutting-edge deep learning models