830 professor-computer-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S" positions at Nature Careers
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program in one of FIMM’s research fields. The positions are targeted to researchers working at the interface of: • statistical methodology, • machine learning and artificial intelligence, • large
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systems and a vibrant interdisciplinary community at Aarhus University, including collaborators in biophysics, neurodegeneration, spectroscopy, and computational modeling. You would join a dynamic research
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professional laboratories, greenhouses, semi-field, and field-scale research facilities, advanced computing capacities as well as an extensive national and international researcher network. The department
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information For further information, please contact: Professor Daniel Otzen, dao@inano.au.dk Application procedure Shortlisting is used. This means that after the deadline for applications – and with
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friendly atmosphere of large and interacting communities for e.g. structural biology, molecular cell biology, computational biology, neurobiology, and molecular medicine that encourages lively, open, and
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computational scientists. The position offers a unique opportunity to work at the interface of landscape ecology, biodiversity science, climate mitigation, and sustainable agriculture, contributing directly to
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The Canada Impact+ Research Chairs (Impact+) program is a one-time initiative designed to support institutions in attracting world-leading researchers whose work addresses critical national and
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research group led by Professor Morten Nielsen in close collaboration with the IEDB leader team at La Jolla Institute for Immunology (LJI), San Diego, California. Your main focus will be centered on further
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university. The postdoctoral researcher will work with Professor Dr Janne Fengler on gaining understandings of contexts and processes of learning and teaching. The ideal candidate has substantive interest in
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position in the area of machine learning and computer simulations. The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement