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. https://doi.org /10.1007/s13280-023-01948-8 ). This postdoctoral position is part of the Marianne and Marcus Wallenberg Foundation-funded strategic research collaboration and postdoc program between
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26 Apr 2024 Job Information Organisation/Company Stockholm University Research Field Environmental science » Natural resources management Researcher Profile Recognised Researcher (R2) Country Sweden Application Deadline 23 May 2024 - 22:00 (UTC) Type of Contract Temporary Job Status Full-time Is...
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skills Contact details for 2–3 references Important: Your references should send us recommendation letters no later than 2024-06-15, via e-mail to postdoc[email protected] and state in the subject line
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and a strong international profile. Project description The postdoc project is part of the Biodiversa+ project DESTRESS (Deciphering temporal trends and safe operating spaces for river biodiversity
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-imaging and competence in data analysis and programming. As a postdoc in the group, you will have the opportunity to develop you own research ideas within the areas of social learning and regulation and the
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leaders and 30 PhD students/postdocs/staff, that is jointly funded by the Swedish Museum of Natural History and the Departments of Geological Sciences, Zoology, and Archaeology at Stockholm University
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-disciplinary and international network between PhD students, postdocs, researchers, and industry Main responsibilities Scientific research and education. The Postdoctoral Fellow is also expected to instruct
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nationwide program of seminars, courses, research visits, and other activities to promote a strong multi-disciplinary and international network between PhD students, postdocs, researchers, and industry Main
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motivated postdoc to join our collaborative project on multiscale simulations & machine-learning (ML) applied to membrane protein function. The candidate will work in the Lab of Prof. Ville Kaila (Stockholm
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Function, Evolution, and Disease. We are searching for a highly motivated postdoc to join our collaborative project on multiscale simulations & machine-learning (ML) applied to membrane protein function