164 computer-science-"https:" "https:" "https:" "https:" "https:" "OSU" Postdoctoral positions in Denmark
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, Life Science Job Type Postdoctoral Employment - Hours Full time Duration Fixed term Qualification PhD Sector Academia Apply on website (This will open in a new window from which you will be automatically
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Investigator, DFF Sapere Aude, Novo Nordisk’s Foundation RECRUIT and Data Science Investigator – Emerging, and Maria Skłodowska-Curie Fellowships. Read more on POLIMA’s website . We offer a competitive and
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, marketing, sustainable consumption, behavioral science, agricultural economics, economics, psychology, or related fields.). The ideal candidate will have experience with one or more of the following
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reviewed academic publications. Is passionate about science and engineering and working in a team dedicated to the green transition. Who we are At the Department of Biological and Chemical Engineering
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-art infrastructure and a strong commitment to scientific excellence. More information: https://mbg.au.dk What we offer We offer: The opportunity to contribute to an internationally recognized
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Computer Science Ref number: 2026/958 Employment type: Full Time Apply Aalborg Deadline : 31.08.2026 Department: Department of Computer Science Ref number: 2026/958 Employment type: Full Time Apply Vacant
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Employer University of Southern Denmark (SDU) Location Odense, Fyn (DK) Salary Competitive Closing date 1 Nov 2026 View more categories View less categories Discipline Environmental Science , Health
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sources and demand profiles. Spatio-temporal optimisation of waste-heat sources and supporting energy technologies integration. Cross-domain collaboration with computer science. This position is part of an
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and should be prepared to work in a collaborative research team with the PI and project affiliates, including interdisciplinary research bridging social and natural science. The successful applicant
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learning. The required qualifications include: Ph.D. in Computer Science, Computer Engineering, Electrical Engineering or a related field; Strong background in Deep Learning (e.g., Transformers, foundation