372 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" PhD positions in United Kingdom
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of WAAMMat (https://waammat.com/ ), gaining valuable industry experience and exposure. The student is expected to acquire the following (including but not limited to) knowledge and skills from the research in
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, water quality and meteorological datasets routinely collected by water utilities. The student will have the opportunity of using state-of-the-art machine learning methods (predictive analytics) to analyse
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. What you should have: A 1st degree in physics or engineering. An interest in optics, some ability in computer programming A desire to learn new skills in complementary disciplines. You will work jointly
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Application deadline: 30/04/2026 Research theme: Nuclear Engineering How to apply: https://uom.link/pgr-apply-2425 This 3.5-year PhD project is fully funded; home students are eligible to apply
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their skills and interests in this research area to https://warwick.ac.uk/fac/sci/eng/postgraduate/funding/ot_epsrc/app/ via the above 'Apply' button. If this initial application is successful, we will invite
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this research area to https://www.warwick.ac.uk/engineeringscholarships/ng_epsrc/app via the above 'Apply' button. If this initial application is successful, we will invite you to submit a formal application
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-funded Fees and Bench fees: Bench fees £1500 Information on fees can be found here: https://www.brookes.ac.uk/study/fees/postgraduate-research-fees/ Start Date: September 2026 Project Title: Dietetic Needs
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hydro-climatic conditions govern vegetation behaviour, and how vegetation impairs the functioning of drainage and water-management assets. Using advanced geospatial modelling, machine learning and digital
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will be based at the University of Birmingham and supervised by Professor Russell Beale and Dr Renate Reniers, who bring expertise in the areas of human computer interaction and psychology respectively
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Subject area: Drug Discovery, Laboratory Automation, Machine Learning Overview: This 36-month PhD studentship will contribute to cutting-edge advancements in automated drug discovery through