110 assistant-professor-computer-"https:"-"https:"-"https:" positions at Aarhus University
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lead the image processing and computational analysis efforts, developing robust methods to register, segment, and analyse spectral micro-CT data, and — where relevant — advance reconstruction and
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advice, and education. We offer professional laboratories, greenhouses, semi-field, and field-scale research facilities, advanced computing capacities as well as an extensive national and international
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The research project Locally Crafted Empires: Intersecting identities under Imperial rule in Western Asia as expressed in local portrait cultures (1st c. BCE-5th c. CE) (LoCiS) headed by Professor
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for environmental challenges, are also encouraged to apply. The postdoctoral researcher will work within an interdisciplinary team of computational and applied scientists. The work will be done in close collaboration
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research, contracted policy advice, and education. We offer professional laboratories, greenhouses, semi-field, and field-scale research facilities, advanced computing capacities as well as an extensive
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computing capacities as well as an extensive national and international researcher network. The department consists of nine research sections with around 350 highly skilled employees, of which approximately
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, Høegh-Guldbergs Gade 2, DK-8000 Aarhus, Denmark. Further information Applicants are encouraged to contact Associate Professor Christoffer Karoff, Department of Geoscience, karoff@geo.au.dk for additional
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, greenhouses, semi-field, and field-scale research facilities, advanced computing capacities as well as an extensive national and international researcher network. The department consists of nine research
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of computational methods. Your job responsibilities As a Postdoc your position is primarily research-based but may also involve teaching assignments. You will contribute to the development of the department through
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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