598 linked-data-"https:" "https:" "https:" "University of St" PhD positions in United Kingdom
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employment policies. For further information on the WIRe scheme visit the web site at: https://cdtwire.com/ The project based at The University of Sheffield will be supervised by academics at Sheffield and
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collaborative research environment, with strong links to the NHS, local authorities, and community partners. During the placement, the student will engage with rich cohort data, and learn about the design
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useful information beyond established clinical risk scores. Phase 2: Linking clinical status to atrial electrical remodelling (n ~100): In a clinical ablation cohort, we will relate standard procedural
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mapping, to rapidly generate linked property-microstructure-chemistry multi-modal datasets, to efficiently produce low‑to‑medium fidelity data streams, which can also be paired to high‑fidelity industrial
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We are recruiting for an exciting research role as a Research Assistant/Data Coordinator, for the Children’s Surgery Outcome Reporting (CSOR) programme. This position is ideal for candidates with
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may be removed before the deadline. Modern low-carbon energy systems such as photovoltaic (PV) arrays and battery energy storage systems (BESS) generate extensive measurement data (electrical, thermal
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Topological Data Analysis for Multimodal Neuroimaging: MRI and NIRS Biomarkers of Brain Change DoS Dr Nathan Broomhead (nathan.broomhead@plymouth.ac.uk, tel.: +441752586874) 2nd Supervisor Dr Mariam
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Applicants should apply via the University’s admissions portal (EUCLID) and apply for the following programme: PhD in ICSA with a start date of 1 September 2026. Applicants should state “Memory Optimisation for Distributed ML Systems” in the ‘Research Topic’ section of the application form, and...
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This PhD project will focus on developing, evaluating, and demonstrating advanced data analytics solutions to a big data problem from aerospace or manufacturing system to uncover hidden patens
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next-generation machine learning (ML) models that are both data-efficient and transferable, enabling more reliable catastrophic risk prediction, defined as the probability of exceeding critical safety