242 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" uni jobs in Denmark
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Tenure-track Assistant and Associate Professorship positions in Algorithms at the Department of M...
interested in learning more about the position, please contact Head of the Algorithms Section Kim Skak Larsen at kslarsen@imada.sdu.dk. Conditions of employment Appointment as a Tenure Track Assistant
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Learning Experience Design The department offers an inclusive and international working environment with state-of-the-art facilities and strong traditions for collaboration across research groups
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at https://www.sdu.dk/mmmi. Curious? If you are interested in learning more about the position, please contact Torben Worm (Head of Centre) tow@mmmi.sdu.dk Mikkel Baun Kjærgaard (Vice Head of Centre) mbkj
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on development of wave energy and offshore wind. Most of our research is focused around work in our wave flume and basin. Further description of the group may be found here: https://vbn.aau.dk/en/organisations
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see: http://ecos.au.dk/en/ . What we offer The department offers: A multi-disciplinary research environment collaboration within strong research teams with extensive experience in carbon flux research
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see: http://ecos.au.dk/en/ . What we offer The department offers: A multi-disciplinary research environment collaboration within strong research teams with extensive experience in carbon flux research
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school outreach activities. Teach in educational programs at the department, mainly within the pharmacy education. Participate in the daily operation, administration and further development
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with core computer science or AI, and must be able to teach computer science courses at the bachelor and master levels. The ACP section Successful applicants will join the section of Artificial
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to new projects. Assist with troubleshooting their machines and support their understanding of core concepts. Guide students in working on their own project. Demonstrate best practices and foster
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joint project with the pathology at OUH. Here, the student shall develop statistical and machine learning approaches to identify potential cancer on whole slide scans. The student contributes