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modelling, satellite data assimilation, multivariate statistics, and machine learning. Prior experience with model and satellite products for mapping and understanding SM-dependent hazards (like floods
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Desirable criteria Experience of advanced statistical and/or machine learning methods, such as longitudinal analysis methods, latent variables models, clustering algorithms, missing data and clinical trial
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are invited for the 2026 Research Fellowship awards. Up to four Research Fellowships will be awarded in this competition. Applicants should have submitted their PhD after 1 October 2024, or be on track
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tools such as R, Python, or MATLAB as well as relevant machine learning frameworks Experience in statistical data analysis, and expertise in areas such as experimental design, linear/nonlinear models
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as world-leading or internationally excellent. We use this expertise to teach the next generation of health professionals and research scientists. Based across King’s Denmark Hill, Guy’s, St Thomas
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, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded methodology project
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scientist looking to apply your expertise to real-world forecasting challenges in Africa? Machine-learning has the potential to revolutionise weather prediction in Africa, and we are seeking a scientist who
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of publications. Criteria Essential or desirable Stage(s) assessed at A PhD (or close to completion of a PhD) in Machine Learning or a similar area (e.g. in Computer Science, Electrical and Electronic Engineering
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modelling and computer simulation to study the earliest stages of embryogenesis in mammals. This full-time post in the group of Dr David Richards at the Living Systems Institute (http://projects.exeter.ac.uk
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bioinformatics Previous experience with AI and/or machine learning approaches Interest in reproductive health and/or development of clinical tools and algorithms Downloading a copy of our Job Description Full