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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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A Clinical Research Fellow (CRF) position in the Nuffield Department of Women’s and Reproductive Health (WRH) is available to start from September 2025. The post is an excellent opportunity
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London School of Hygiene & Tropical Medicine; | London, England | United Kingdom | about 1 month ago
). This global project aims to assess the behavioural and social drivers of vaccine uptake and to identify both supply- and demand-side barriers to immunisation. The post-holder will contribute to modelling
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engineering, including machine learning, sustainable construction, climate adaptation, and intelligent tools. Demonstrate future contributions to capacity-building and socio-economic advancement after
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proven interest in AI foundations and its application in civil and environmental engineering, including machine learning, sustainable construction, climate adaptation, and intelligent tools. Demonstrate
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agents, including uncertainty quantification at the agent’s level. The project will bring together ideas from Statistics, Probability, Statistical Machine Learning, Statistics and Game Theory and is an
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degree (MD, PhD or equivalent) in a relevant subject area: must have submitted PhD thesis before taking up the post Demonstration of understanding of, and commitment to, an academic career Evidence of team
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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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needed. The role will include responsibilities ranging from design, delivery and execution of the clinical trial, ensuring the safe conduct and collection of clinical data. The post provides an ideal
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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