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underlying physics while enabling real‑time or near‑real‑time predictions. You will work with experts in engineering, CFD, data-driven fluid dynamics and computer science. This PhD provides an excellent
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Research theme: Analytical Chemistry, Laser Physics, Materials Characterisation How to apply: uom.link/pgr-apply-2425 This 3.5-year PhD studentship is open to Home (UK) applicants and EU students
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, inspection histories, operational performance data, hyperspectral imagery, train-borne video analytics and satellite soil-moisture products to build predictive models of vegetation-driven and water-driven
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: There will be a shortlisting and interview process. RCUK eligibility: No Eligibility: Applicants should hold, or expect to obtain, a postgraduate degree in Computer Science, Artificial Intelligence, Data
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on the project choice/previous experience/interests, as a PhD student, you will gain hands-on experience in Synthetic chemistry and analytical techniques (HPLC, LC–MS, NMR) Designing and synthesising compounds
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that deliver healthier indoor environments, lower carbon emissions, and long-term building performance. By integrating Passive House and EnerPHit principles with real building data, the research will support the
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. The PhD will adopt a strongly quantitative and data-driven approach. The student will develop and evaluate mathematical and statistical models of short-term attention dynamics, drawing on tools from
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Dalton Building at Manchester Metropolitan University. You will develop skills in computational experimentation, financial data analytics, and systemic risk modelling, with extensive opportunities
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Applications are invited for an ESRC-funded North West Social Sciences Doctoral Training Partnership (NWSSDTP) PhD studentship (with potential to apply for 1+3 award) at the University of Central
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date 1 October 2026 Additional Funding Information This PhD project is funded for four years by Big C with a contribution of £50,000 supported by the John & Pamela Salter Charitable Trust. Funding