33 machine-learning-"https:" "https:" "https:" "https:" "U.S" Postdoctoral positions at Aarhus University in Denmark
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refer to http://mbg.au.dk/ for further information about The Department of Molecular Biology and Genetics and to https://nat.au.dk/ and http://www.au.dk/ for information on Faculty of Natural Sciences
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Applications are invited for a postdoctoral position in the group of Dr Aleksandr Gavrin ( https://mbg.au.dk/a-gavrin/ ) at the Department of Molecular Biology and Genetics, Aarhus University
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fishing activities, major shipping routes, and offshore development locations. The EU Oceans Pact highlight the need to assess and manage dumped munitions. Two EU-funded projects, MUNI-RISK ( https://muni
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the following areas, biomedicine, immunology, neurology, medicine, biology, or other, related fields. Experiences in microfluidics or single-cell analysis are a plus, but not a prerequisite to learn our methods
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targeted at career development for postdocs at AU. You can read more about it here: https://talent.au.dk/junior-researcher-development-programme/ If nothing else is noted, applications must be submitted in
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collaborative relationships. Read more about the Department of Food Science at: https://food.au.dk/ The place of work is Department of Food Science, Aarhus University, Agro Food Park 48, Skejby, 8200 Aarhus N
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programming experience with Python and/or Matlab Experience with advanced MEG/EEG signal processing and/or machine learning is desirable Strong interest in translational research and developmental neuroscience
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analysis to translate THz signals into optical material properties such as refractive index and absorption coefficient. Development of machine learning algorithms for material classification. Exploration
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Research Focus We are offering a Postdoctoral position in graph machine learning, algorithms, and graph management with particular focus on: Modeling real-world spatio-temporal energy networks Developing
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-constrained machine-learning (ML) models in simulations of turbulent flows. You are expected to contribute to research and development in data-driven methodologies for turbulence modeling in LES (i.e., wall and