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Field
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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
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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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; Midlands Graduate School Doctoral Training Partnership | Nottingham, England | United Kingdom | 3 months ago
the consumption journey. This exciting PhD Programme will look to address these challenges by employing a novel combination of data-donation, data-enhancement, and data-augmented laboratory experiments. The project
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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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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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This PhD project will investigate the recent field of study of Causal Machine Learning, which aims to modify and augment Machine Learning by using Causal Analysis techniques as a way to solve its