34 computational-physics "https:" "https:" "https:" "https:" positions at Aarhus University
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2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The Department
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an experience in technology-assisted monitoring or computational image analysis. Expected start date and duration of employment The position will start in June 2026, with exact starting date as agreed between
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job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The Department
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at the Department of Physics. The position is available from the 1st of May 2026, or as soon as possible hereafter. Job description Your research will look at a special class of porous metal-organic frameworks (MOFs
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strong emphasis on publishing in leading academic journals and presenting at recognised conferences. You can read more about the Department of Management at: http://mgmt.au.dk . Contact information Further
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The project will involve both experimental and computational work and the candidate is expected to be comfortable with both. The candidate is expected to have (or be close to finishing) a Ph.D. in molecular
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and technical-administrative staff and you have a flair for establishing collaborative relationships. Read more about the Department of Food Science at: https://food.au.dk/ The place of work is
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@au.dk) Applicants must have a relevant PhD degree in biology, biogeochemistry, hydrology, glaciology, oceanography, geoscience or physics. Field experience, data analysis and programming (e.g., python
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will report to Professor of Medical Physics Stine Korreman. Your competences You have academic qualifications at PhD level, for example within the following areas; computer science, biomedical
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spatially explicit process-based ecological model (DEB-IBM) for muskoxen in the high-Arctic through the analyses of long-term GPS and acceleration data. using the model to estimate the (cumulative) impacts