176 evolution "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "St" "St" "St" positions in Finland
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programs The University of Helsinki provides its staff with occupational health and sports services as well as professional development opportunities (https://www.helsinki.fi/en/about-us/careers/why
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for the development of researchers’ working conditions and careers. Our Buddy Programme and Spouse Network support you and your close ones in settling into Oulu. If you are moving from abroad, we offer comprehensive
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resources for research including LUMI supercomputer and close collaboration with companies and the ELLIS network. Read more and apply by February 9 at https://www.ellisinstitute.fi/postdoc-recruit-2026 Where
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the Finnish-language job announcement for further information: https://ats.talentadore.com/apply/tutkijatohtori-sustainable-rural-futures-hanke/m5npoR Department of History and Ethnology The Faculty
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the Finnish-language job announcement for further information: https://ats.talentadore.com/apply/vaitoskirjatutkija-sustainable-rural-futures-hanke/813zEq Department of History and Ethnology The Faculty
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, Pseudomonas aeruginosa, and Enterobacter spp). Our goal is to make a difference in the development of next-generation strain-specific rapid diagnostics, and to help find potential drug/vaccine targets. We have
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artificial intelligence/geospatial AI, methods of machine learning and deep learning development of computer vision applications and image recognition methods analysis and production of big data, including
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fields. Your research can be theoretical, applied, or span both. Deadline February 9, 2026. Where to apply Website https://jobrxiv.org/job/ellis-institute-finland-27778-postdocs-in-machine-learn
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at coordinator@hiit.fi Where to apply Website https://jobrxiv.org/job/helsinki-institute-for-information-technology-27778-pos… Requirements Additional Information Work Location(s) Number of offers
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-effectively predicting the rate of massively multicomponent organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning