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Jan. 2026, based in the University of Birmingham UK. This position will use further develop the novel AI/machine-learning (ML) approach in Chen et al. (2022 & 2024, Nature Geoscience ) and apply
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experience in spatial analysis and/or machine learning methods, and an interest in applying these tools to urban and housing policy questions. The Fellow should demonstrate potential for producing high-quality
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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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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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, written, and oral communication skills in English. Exhibit strong organisational skills and the ability to meet deadlines and complete projects. Have expertise in machine learning and/or programming (highly
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to undertake world-leading research in the design, integration and Edge-implementation/testing of multimodal machine learning models. Your experience in real-time implementation of federated AI and Edge-based
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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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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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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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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