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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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Research theme: Bioinformatics, Machine learning, Healthcare How to apply:uom.link/pgr-apply-2425 Number of positions: 1 This 3.5 year PhD is fully funded. The successful applicant will receive
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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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the research areas of Probabilistic Machine Learning, Neuro-Symbolic AI and Deep Generative Models. The PhD candidate will research the methodological foundations for a new generation of probabilistic
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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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; Midlands Graduate School Doctoral Training Partnership | Nottingham, England | United Kingdom | 3 months ago
ESRC DTP Strategic Joint Studentship University of Nottingham and University of Birmingham The Midlands Graduate School is an accredited Economic and Social Research Council (ESRC) Doctoral Training Partnership (DTP). One of 15 such partnerships in the UK, the Midlands Graduate School is a...
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, while smart meter data will refine localisation for greater precision. Machine learning (ML) algorithms will analyse these datasets to deliver a scalable, cost-effective system, validated through field
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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