12 machine-learning-"https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" uni jobs at BIOMEDICAL SCIENCES RESEARCH CENTRE "ALEXANDER FLEMING" in United Kingdom
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About us University College London (UCL) is London’s leading multi-disciplinary university. UCL’s research and teaching is organised within 11 faculties, each housing a number of departments
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https://www.ucl.ac.uk/work-at-ucl/rewards-and-benefits to find out more. Our commitment to Equality, Diversity and Inclusion We particularly encourage applications from candidates who are likely to be
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to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust, interpretable models from experimental and operational data
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with the CDT’s aim to achieve a sustainable wind farm lifecycle by developing methods for high-value reuse of composite turbine blades. Machine learning and non-destructive evaluation techniques will be
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of the resultant analysis. This will also involve the collection of a curated data set, and the use of Machine learning tools to further enhance the analytical process. Together, this will help expand the use
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or above with at least 6.0 in each component, or equivalent. Please see this link for further information: https://www.sheffield.ac.uk/postgraduate/phd/apply/english-language How to apply Please complete
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on an NIHR funded research project - Digital support for siblings of children with learning disabilities: Feasibility Randomised Controlled Trial of the serious game ‘Broodles’. This project examines
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the ‘Intellectual Disabilities Research Institute’. We call it IDRIS. IDRIS is a group of researchers at the University of Birmingham. IDRIS does research to help people with learning disabilities. IDRIS does
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of children with learning disabilities: Feasibility Randomised Controlled Trial of the serious game ‘Broodles’. This projects examines whether a serious game (Broodles) can be delivered successfully to siblings
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staff and students to work and learn. We positively encourage applications from suitably qualified and eligible candidates from diverse backgrounds, including race, disability, age, sex, gender identity