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Research Assistant to investigate digital reading comprehension through large-scale data analysis. This role, funded by the John Fell Fund, sits at the fascinating intersection of computational
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for undergraduate courses and research programmes and provide guidance to other members of staff within the research team • To maintain and update area of specialist knowledge, researching and critically
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PhD programmes and looking for hands-on research experience on high-impact questions in energy and environmental economics. Key Responsibilities for the role include: Data collection, cleaning, and
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such as computational social science, computer science, or cognitive science. They will have a demonstrable track record in studying humans and machine learning models, in the context of human social
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process simulation, techno-economic analysis (TEA), and life cycle assessment (LCA) methodologies Strong computational skills, including proven expertise in programming environments (e.g., Python, Matlab
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You You should have (or be due to submit soon) a PhD, or have equivalent experience, and have expertise in mathematical and computational modelling of fluid dynamic phenomena. Ideally, you will have
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, computer science, statistical or population genetics, or a related discipline), and a strong motivation to work on problems in genetics and you will also have relevant coding experience with producing high-quality
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We are seeking two full-time Postdoctoral Research Assistants in Machine Learning to join the Foerster Lab for AI Research group at the Department of Engineering Science (central Oxford). The post is funded by ERC (or UKRI) and is fixed term to 31 December 2028. We are seeking two candidates...
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. statistics, mathematics, computer science, statistical or population genetics, or a related discipline), and a strong motivation to work on problems in genetics and you will also have relevant coding
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, mineralogical phase characterisation of materials, lab-based testing on electrochemical techniques, microstructure performance evaluation and computational modelling. You will be self-motivated with a proven