255 data-"https:" "https:" "https:" "https:" "https:" "https:" "Dr" "UCL" "UCL" "UCL" Postdoctoral research jobs in Sweden
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13 Feb 2026 Job Information Organisation/Company KTH Royal Institute of Technology Research Field Computer science » Informatics Computer science » Programming Computer science » Other Educational
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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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information about the position, please contact: Dr. Chao Zhang (chao.zhang@kemi.uu.se and +4618-471 3721), Dr. Rasmus Andersson (rasmus@compulartech.com ) Please submit your application by 2026-02-12, UFV-PA
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most two pages, explaining your motivation, research goals and why you fit the advertised position. The workplace The Department of Computer and Information Science was founded in 1983, but its roots go
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. Information about the workplace: https://liu.se/en/organisation/liu/ifm https://liu.se/en/research/m2lab The employment This employment is a temporary contract of two years with the possibility of extension up
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the health and MedTech sectors, and beyond. For more information about the total announced post-doctoral positions within in the AMBER co-fund project please visit https://www.euraxess.se/jobs/392999
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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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within the Data Science & AI division (DSAI). With 30+ nationalities and strong industry/academic ties, we offer a dynamic, collaborative ecosystem. The AI and Machine Learning in the Natural 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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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