92 information-security "https:" "https:" "https:" "https:" "https:" "UCL" research jobs at Aarhus University
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place of work is Department of Mathematics, Faculty of Natural Sciences, Ny Munkegade 118, 8000 Aarhus C. The complete job advertisement is available at: https://international.au.dk/about/profile/vacant
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Department of Management, please visit: http://mgmt.au.dk/ . Further information For further information about the position and the department, please contact Assistant Professor Gabriele Torma, email
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This is a full-time (37 hours/week) on-site role located in Aarhus, Denmark for a Postdoctoral Research Fellow at the Department of Computer Science, Aarhus University. This position is for 2 years
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The Centre for Computational Thinking and Design at the Department of Digital Design and Information Studies within the School of Communication and Culture at Aarhus University invites applications
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recommendation by CNN (https://edition.cnn.com/travel/article/aarhus-denmark-things-to-do/index.html ). Aarhus is easily reached via local international airports in Jutland within 1 hour of Aarhus, or via
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We invite applications from researchers to join our XR (extended reality) and Visual Computing research groups. We have 3 posts available from 1 May but there is flexibility for later start dates
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. You can read more about the Department of Management at: http://mgmt.au.dk . Further information For further information about the position and the department, please contact the Director of the Center
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information about the department; https://anivet.au.dk/en Contact Further information on the position may be obtained from Senior Researcher Stig Purup, phone: +45 29345955 , email: stig.purup@anivet.au.dk
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of Animal and Veterinary Sciences (au.dk) for further information about the department; https://anivet.au.dk/en Contact Further information on the position may be obtained from Head of Research Unit Peter
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Join us at the Department of Electrical and Computer Engineering at Aarhus University for a postdoctoral position focused on deep learning based analysis of remote sensing data for groundwater