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) Contact information for two references. Applications should be sent to Prof. Alexandre Steiner by e-mail ([email protected] ). Review of applications will begin immediately and will continue until
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). This interdisciplinary project investigates how AI — specifically large language model (LLM)-based agents — can act as adaptive social agents to support students' collaborative learning in Challenge-Based Learning (CBL
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of a large team, including computational and experimental postdocs, PhD students and a research assistant, with access to cutting-edge experimental and computational facilities in the newly opened Life
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 5 hours ago
Posting Information Posting Details Department Physics and Astronomy - 319000 Posting Open Date 10/01/2026 Application Deadline 02/01/2027 Open Until Filled No Position Type Postdoctoral Scholar
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implementing novel data analysis and statistical methods for Type Ia supernova cosmology, and contributing to data acquisition and spectroscopic follow-up of upcoming large transient surveys (e.g. Rubin/ATLAS
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, spatial statistics, machine learning approaches, large ecological datasets Previous experience with passive acoustic monitoring and/or eBird data Familiarity with ecology and/or ornithology Demonstrated
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collaboration with our wet-lab team and collaborators, you identify and integrate validation data In collaboration with the VIB.AI ML unit, you manage a large codebase containing the harness, interpretation
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analysis of metabolites; process and evaluate experimental data; develop and validate analytical methods; prepare scientific manuscripts; collaborate with internal and external researchers; Where to apply E
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4 Sep 2026 Job Information Organisation/Company Uppsala universitet Department Uppsala University, Disciplinary Domain of Science and Technology, Faculty of Chemistry, Department of Chemistry
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computational experiments and data pipelines Experience analyzing complex experimental data and large-scale datasets using advanced quantitative and statistical methods Track record of independent scientific