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Act 2006. Full-Time, Fixed-Term until 30 November 2028 Applications are invited for the post of Post Doctoral Research Associate (PDRA) in the Department of Computer Science at Royal Holloway. This is a
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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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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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The post holder will join the Physical Oceanography and Palaeoclimate groups in the Department of Earth Sciences and perform numerical model data analysis relevant to two projects: MEZCAL and PalaeoGradPhan. Both projects focus on the relationship between ocean circulation and surface ocean...
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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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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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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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, 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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reduce computational load and improve adaptability in ISAC systems. ICS is the largest academic research institute in Europe specialising in all aspects of ICT (Information and Communication Technologies
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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