57 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" Postdoctoral positions at Nature Careers in Denmark
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research group working on population, conservation and landscape genomics led by Professor Michael M. Hansen (https://www.au.dk/en/mmh@bio.au.dk), will become a member of the Section for Genetics, Ecology
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19165 Post-Doctoral Fellowship in risk assessment and prioritization and remediation of dumped mu...
fishing activities, major shipping routes, and offshore development locations. The EU Oceans Pact highlight the need to assess and manage dumped munitions. Two EU-funded projects, MUNI-RISK (https://muni
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-supervision, teaching and day-to-day work of the group. The place of work is Aarhus University Energy Research Facility located at AU Campus Viborg (https://dca.au.dk/en/about-dca/au-foulum ) and Gustav Wieds
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integrates, develops, and disseminates knowledge of farm animal management and relationships between actors and animals. Researchers in the section teach BSc, MSc and PhDs in animal and veterinary science
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collaborative relationships. Read more about the Department of Food Science at: https://food.au.dk/ The place of work is Department of Food Science, Aarhus University, Agro Food Park 48, Skejby, 8200 Aarhus N
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and to maintain and attract talented and committed staff, where freedom, creativity and respect for the long-term perspective are core values. For more information on the Department see: http
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welcoming environment for children - a great place for the whole family. For further details on the city and the university please follow this link: https://international.au.dk/ . Place of work The place of
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Application deadline December 1, 2025, at 23:59 CET Apply online https://fa-eosd-saasfaprod1.fa.ocs.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1001/jobs/preview/3144
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biogeochemical modelling and data-driven machine learning approaches at an ecosystem scale to improve our understanding of the fate of nitrogen fertilizers applied to agricultural soils. This understanding will be
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-constrained machine-learning (ML) models in simulations of turbulent flows. You are expected to contribute to research and development in data-driven methodologies for turbulence modeling in LES (i.e., wall and