19 molecular-modeling-or-molecular-dynamic-simulation positions at University of Copenhagen in Denmark
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We are looking for one or more highly motivated and dynamic research assistants for fixed term position(s) at the Section of Biostatistics, Department of Public Health to commence November 1st 2025
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, computational chemistry or similar. Our group and research- and what do we offer? In our group we work with theoretical catalysis, which is based on atomic scale simulations and dynamics. In this way we have
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conditions in dynamic and international research environment. Principal supervisor (PI) is Professor Pernille Bjørn, Department of Computer Science, pernille.bjorn@di.ku.dk The PhD programme Option A: Getting
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at the Niels Bohr Institute, Faculty of Science, University of Copenhagen. We are located in Copenhagen. We offer creative and stimulating working conditions in a dynamic and international research environment
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at the University of Copenhagen. The working language at BRIC is English. Our group and research The Ochs group (https://www.bric.ku.dk/research-groups/Research/ochs-group/) aims is to decipher the molecular
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programmers. You will also work closely together with course teams to develop data generation, data analysis, modeling, simulation, and machine learning workflows as well as develop custom data science-related
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of Copenhagen. We offer creative and stimulating working conditions in dynamic and international research environment. Terms of employment The employment as PhD fellow is full time and for 3 years. It is
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forest research group, you will join a dynamic research environment that spans forest management, silviculture, forest modelling, climate adaptation, and ecosystem monitoring. You will collaborate across
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author in a related area Fellowships, grants and prizes Place of employment The place of employment is at BRIC, University of Copenhagen. We offer creative and stimulating working conditions in dynamic and
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collaboration with survey providers in Denmark and Germany. Analyzing survey data using quantitative methods such as conjoint analysis, regression modeling, and causal inference techniques. Writing academic