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-informed learning) with hard physical constraints (Navier–Stokes in spectral space) we will develop methods to super-augment experimental data via data assimilation and turn sparse wind-tunnel measurements
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Applications are invited for a fully funded PhD (fees and stipend) to begin March 2026 on a research project developing an adult version of the Behaviour Checklist, a tool originally developed for
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-time Start date 1 October 2026 Funding Information: This PhD project is in a competition for a Faculty of Science funded studentship. Funding is available to UK applicants and comprises ‘home’ tuition
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We are recruiting a PhD student to integrate data-driven methods with NMR spectroscopy to enhance the characterisation of cell culture media and metabolites, increasing throughput and reducing
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. Eligibility Applicants should have a first or upper second class honours degree or a relevant Masters qualification. This PhD is suitable for individuals with degrees in Geology/Earth Sciences with interests
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multimodal data, ultimately uniting rigorous machine learning foundations with biological discovery. Project details This PhD project will contribute to the development of generative models for multimodal data
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honours degree (or equivalent) in a relevant discipline such as biology, ecology, microbiology, plant sciences, bioinformatics, or data science. The project is particularly suitable for students with
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. The standard minimum entry requirement is 2:1 in Computing science or related fields. Mode of Study Full-time Start Date 1 October 2026 Funding Information: This PhD project is in a competition for a Faculty of
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The London Interdisciplinary Social Science Doctoral Training Partnership (LISS DTP) is led by King’s College London and funded by the Economic and Social Research Council (ESRC) The LISS DTP Open
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One fully funded, full-time PhD position to work with Prof. Mahesh Marina in the Networked Systems Research Group at the School of Informatics, University of Edinburgh. The broad aim