136 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Washington-University-in-St" positions in Sweden
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for organisations and individuals. For more information about our department and research groups, visit the Department of Computer Science and Informatics (switch to the Swedish version for all bachelor programmes
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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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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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Join us to develop physics-guided, data-driven foundation models for multivariate time-series in safety-critical systems, with a primary focus on automotive applications. You will design predictive
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machine data analytics to new harvesting system designs. The Department of forest genetics and plant physiology is part of Umeå Plant Science Centre (UPSC, https://www.upsc.se ), a world leading centre for
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NAISS, the National Academic Infrastructure for Supercomputing in Sweden, provides academic users with high-performance computing resources, storage capacity, and data services. NAISS is hosted by
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16 Jan 2026 Job Information Organisation/Company Karolinska Institutet, doctoral positions Research Field Medical sciences » Medicine Researcher Profile First Stage Researcher (R1) Application
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13 Dec 2025 Job Information Organisation/Company Karlstads universitet Department Karlstad University / Handelshögskolan Research Field Information science » Information management Researcher
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Facilities, Sahlgrenska Akademin in Gothenburg, Sweden. Selection process For more information regarding the selection process go to: https://www.gu.se/om-universitetet/jobba-hos-oss/hjalp-for-sokande#S%C3%A5
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at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models