176 evolution "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" positions in Finland
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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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research group of Professor Juho Rousu. The position allows the successful applicant to choose a flexible balance between advanced machine learning method development and exciting applications on molecular
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documentation What we offer A friendly, international research environment with high-quality infrastructure Training opportunities and professional development Comprehensive employee benefits (occupational health
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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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, 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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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
predictive models for complex multi-physics dynamical systems as well as towards designing observer-based state estimators from output timeseries data measurements. The research also involves development
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of Helsinki offers comprehensive services to its employees, including occupational health care and opportunities for professional development. Please read more at https://www.helsinki.fi/en/about-us/careers
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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