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international collaborations that support the transition to a low-carbon, resilient and environmentally responsible future. Our research spans three key areas: Blue/Green Infrastructure: restoring, managing and
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-organize, understand and model their surroundings, and autonomously generate efficient paths and formations while adapting to changing environmental conditions. The position also involves designing and
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16 Jul 2026 Job Information Organisation/Company Universitat de Barcelona Department Organisation profile Research Field Agricultural sciences Biological sciences Computer science Environmental
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childhood asthma and lung function. Toxicology, led by the Texas A&M School of Public Health, where we will investigate underlying biological mechanisms using standard and novel (mito-QC) mouse models
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support for all. We strongly encourage applications from under-represented groups. Main duties and responsibilities The project is conducted in collaboration with the Environmental Fluid Mechanics
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Job no:523342 Work type:Full Time Location:Crawley Categories:Engineering and Mathematical Sciences A highly technical postdoctoral engineering role focused on rock mechanics, modelling, and AI
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Potsdam-Institut für Klimafolgenforschung e.V. (PIK) | Potsdam, Brandenburg | Germany | about 2 months ago
explaining observed temporal variations and trends in internal and/or international migration building on state-of-the-art econometric and/or system dynamics modeling approaches and recent migration data
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, LeafWeb, Sapfluxnet, PSInet) to translate trait variation into model parameter priors and functional constraints, and to explore parameter relationships with environmental conditions Hybrid modeling
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, including extensive fieldwork, travel, heavy lifting, and working in extreme environments. Performs other duties as assigned. Required Qualifications: Ph.D. in soil science, microbiology, environmental
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seeking a highly motivated Postdoctoral Researcher to join the Machine Learning cluster. The position is part of a research project investigating how visual foundation models can efficiently acquire new