30 machine-learning-postdoc-"https:" "Naturalis" Postdoctoral positions at Aarhus University
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will
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University also offers a Junior Researcher Development Programme targeted at career development for postdocs at AU. You can read more about it here . The application must be submitted via Aarhus University’s
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We are seeking applicants for a 2-year postdoc in Ultrafast X-ray probes of Quantum Materials to join us at the Department of Physics and Astronomy. Starting Date and Period The position is for 2
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key agroecosystem variables. These variables include cover crop growth, crop nitrogen, yield, and tillage practices. You will develop novel algorithms to integrate data-driven machine learning and
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The Daasbjerg research group at the Department of Chemistry, Aarhus University, is seeking a candidate for a 31-month postdoctoral position. This position focuses on AI/machine learning to develop a
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collaborative work environment, with regular scientific exchange and knowledge-sharing that helps lab members learn from each other and move projects forward efficiently. Read more:Peter Zeller What we offer
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The Department of Clinical Medicine, Danish Center for Particle Therapy, at Faculty of Health at Aarhus University invites applications for a position as Postdoc in the field of AI for Imaging in
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atwww.international.au.dk/ Where to apply Website https://au.career.emply.com/en/apply/postdoc-in-structural-biology/0sxo68 Requirements Research FieldBiological sciencesEducation LevelPhD or equivalent Additional
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The Centre for Science Studies, Aarhus University is inviting applications for a postdoc position within the field of history of science and ideas. The position is a fixed-term position available
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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