948 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" "UNIV" "UNIV" "UNIV" positions in Sweden
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3 Jan 2026 Job Information Organisation/Company Lunds universitet Department Lunds universitet, Kemiska institutionen (Nfak) Research Field Chemistry » Physical chemistry Researcher Profile First
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28 Jan 2026 Job Information Organisation/Company Göteborgs universitet, Department of Marine Sciences Research Field Geosciences Researcher Profile First Stage Researcher (R1) Application Deadline
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competence in image analysis and data processing. What you will do Develop and optimize experimental workflows, including sample preparation under different environmental conditions. Design and conduct
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13 Jan 2026 Job Information Organisation/Company Stockholm University Department Department of geological sciences Research Field Geosciences » Geology Chemistry » Biochemistry Biological sciences
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6 Mar 2026 Job Information Organisation/Company Luleå University of Technology Research Field Other Researcher Profile First Stage Researcher (R1) Application Deadline 3 May 2026 - 12:00 (UTC
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physics can unlock radically new ways of processing information - far beyond the limits of classical systems. Our research spans quantum computing, sensing, transduction, thermodynamics, and foundations
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development, testing and application of the LPJ-GUESS biosphere model for modelling tropical wetlands and estimating tropical methane emissions. The work is part of the EU-funded project IM4CA (https://im4ca.eu
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15 Feb 2026 Job Information Organisation/Company Lunds universitet Department Lund University Research Field Sociology Researcher Profile First Stage Researcher (R1) Application Deadline 10 Mar 2026
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28 Feb 2026 Job Information Organisation/Company Lunds universitet Department Lund University Research Field Biological sciences » Biology Researcher Profile First Stage Researcher (R1) Application
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This project targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification