53 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" research jobs at Chalmers University of Technology in Sweden
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We are looking for an ambitious postdoctoral researcher to advance the integration of microbial biodiversity and data-driven innovation for next-generation food fermentation within the MIND
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and Data Analytics, Fraunhofer-Chalmers Centre E-mail: mats.jirstrand@fcc.chalmers.se Phone: +46 730 794303 URL to this page https://www.chalmers.se/en/about-chalmers/work-with-us/vacancies/?rmpage=job
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Join and help us to derive global forest biomass data from the European Space Agency’s Biomass satellite mission. If you have interests in remote sensing, machine learning and forests, this is the
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and Data Analytics, Fraunhofer-Chalmers Centre E-mail: mats.jirstrand@fcc.chalmers.se Phone: +46 730 794303 Where to apply Website https://academicpositions.com/ad/chalmers-university-of-technology/2026
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28 Feb 2026 Job Information Organisation/Company Chalmers University of Technology Research Field Computer science » Other Engineering » Control engineering Engineering » Systems engineering
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Research FieldPhysicsYears of Research Experience4 - 10 Research FieldTechnologyYears of Research Experience4 - 10 Additional Information Website for additional job details https://academicpositions.com Work
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it effect engagement and learning. For more information about the Akelius Math Learning Lab, see: https://www.chalmers.se/institutioner/mv/akelius-math-learning-lab/ Who we are looking
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FieldEngineeringYears of Research Experience4 - 10 Research FieldTechnologyYears of Research Experience4 - 10 Additional Information Website for additional job details https://academicpositions.com Work Location(s
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Experience4 - 10 Research FieldPhysicsYears of Research Experience4 - 10 Research FieldPhysicsYears of Research Experience4 - 10 Additional Information Website for additional job details https
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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