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4-year D.Phil. studentship Supervisors: Dr Simone Falco, Prof Daniel Eakins Classic finite elements approach (FEA) approximate the shape of the model using elements with planar faces, therefore
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Research theme: “Groundwater remediation”, “Environmental application of nanotechnology”, “Groundwater modelling”, “Aquifer”, “MODFLOW model”, “Nanoparticle transport in porous media”, “Nanoparticle
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the UKRI rate (£19,237 for 2024/25) and tuition fees will be paid. We expect the stipend to increase each year. Generative models, such as large language models (LLMs), have exhibited remarkable success in
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the transition to a full-time training model, and how these demands can be best managed to maximise adaptations and performance, and mitigate injury risk. The successful candidate will audit the current scientific
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This PhD scholarship is for candidates interested in developing culturally inclusive AI by creating methods and datasets to align large language models with global social and legal norms. This PhD
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to remote sensing and simu-lation modeling. A particular focus of our work is on mountain forest ecosystems. A quantitative understanding of ecosystem dynamics provides the foundation for the development
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In this PhD project, we will develop and implement approaches for estimating the uncertainty in AI predictions of chemical reactivity, to help strengthen the interaction between human chemists and machine learning algorithms and to assess when AI predictions are likely to be correct and when,...
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PhD Studentship: Knowledge models for healthcare digital twins and improved patient care pathways School of Mechanical, Aerospace and Civil Engineering PhD Research Project Directly Funded Students
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for individuals living with multiple long-term conditions known as multimorbidity, and may lead to unnecessary polypharmacy. This PhD studentship aims to develop a Bayesian modelling framework to identify clusters
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health with the aim of creating healthier spaces and habits for them. This is a highly interdisciplinary project that combines computational modelling and behavioural science. The first part will be based