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information For further information, please contact: Assoc. Prof. Marianne Glasius, +45 2127 5261, glasius@chem.au.dk. Application procedure Shortlisting is used. This means that after the deadline
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, processing, and analyzing remotely sensed data. Documented experience in scientific writing and publication in peer-reviewed scientific journals. Capacity to work independently and collaboratively within a
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, processing, and analyzing remotely sensed data. Documented experience in scientific writing and publication in peer-reviewed scientific journals. Capacity to work independently and collaboratively within a
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collaboration between research groups at several departments at Aarhus University. For further information, please contact Professor Kurt Gothelf +45 60 20 27 25 kvg@chem.au.dk Application procedure Shortlisting
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project. Your profile We are looking for a highly motivated candidate with a background in machine/deep learning, and communication networks. The required qualifications include: PhD in computer engineering
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observations, and remote sensing data to assess the impact of global change on ecosystem productivity and sustainability. You will develop novel algorithms to integrate data-driven machine learning and process
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reached – namely when a certain level of non-economic losses and damages avoidance has been secured – a community may decide that funding is better spent on other issues. Both the type of information
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WorkinDenmark for additional information. Further information about The Faculty of Engineering. Application procedure Applicants are advised to read the SDU information on how to apply . The application
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Quantitative/Empirical Methods and Data Science Welfare: Education, Health Care and Pensions. The Department of Economics has a strong research focus and is very successful in fundraising activities. It is
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huge opportunity as an untapped resource. You will contribute to an interdisciplinary team comprising experts in structural biology, biophysics, microbiology, biopolymer science, downstream processing