172 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Mines Paris PSL" positions in Finland
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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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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
to develop machine learning-enabled approaches for predictive modelling and state estimation for fundamental applications within physical sciences. Your role The main research responsibilities involve building
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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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The Faculty of Law at the University of Helsinki is seeking a University Lecturer for a fixed-term position from September 1, 2026, to August 30, 2028, who can teach public law, particularly
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The Faculty of Law at the University of Helsinki is seeking a University Lecturer for a fixed-term position from January 1, 2027, to April 30, 2028, who can teach criminal law and procedural law
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of educational work Practical experience in teaching and efforts to improve teaching, digital teaching skills, and giving feedback, possible participation in continuous learning provision, and international
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the University of Helsinki and living in Finland, please see https://www.helsinki.fi/en/about-us/careers . A diverse and equitable study and work culture is essential to us. That is why we do our best to promote
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-Pohjanmaa Country: Finland Reference number: 2026/124 Published: 2026-03-04 Last application date: 2026-04-30 Where to apply Website https://academicpositions.com/ad/university-of-oulu/2026/professor
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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