84 computer-"https:" "https:" "https:" "https:" "https:" "Inria" Postdoctoral positions at University of Copenhagen
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The department of computer science (DIKU) invites applicants for a 24 month postdoctoral fellowship in stochastic models of morphology evolution. The postdoc will perform research in relation to the
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the Department of Geosciences and Natural Resource Management and the Department of Computer Sciences at the University of Copenhagen, DHI A/S, and GEUS (The Geological Survey of Denmark and
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a civic-minded researcher with human-computer interaction skills to join the group. A well-documented problem with the current AI systems is the lack of factuality, but little is done to consider
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Education and the Department of Computer Science. It is not a requirement to be able to teach in Danish. UCPH has an International Staff Mobility office that assists international scholars with their
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and Robust Automatic Fact Checking (ExplainYourself)’, as well the Pioneer Centre for AI. Who are we looking for? Applicants should hold a PhD degree or equivalent in Computer Science or a related
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and industry partners in Denmark and abroad Qualifications We are looking for a candidate who has: A PhD in computer science, bioinformatics, food science, or a related field Documented experience
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in the Memorandum on Job Structure for Academic Staff at Danish Universities 2019 here: https://employment.ku.dk/faculty/criteria-for-recognising-merit/dokumenter/Ministerial_Order_no
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collaborators. We believe that cooperation and collegiality are essential elements for research successes. You can find more information about the team and our work here: https://www.bric.ku.dk/research
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Copenhagen ( https://plen.ku.dk/english/research/plant_biochemistry/be/ ) . The research group, headed by Prof. Sotirios Kampranis ( Sotirios Kampranis) , explores and engineers nature’s sensing
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, https://sweichwald.de/#research-with-us ). Applicants are encouraged to describe a concrete research direction, what they bring to it, and why it fits the group’s work. Causal Inference and