668 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" positions in Denmark
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application of machine and deep learning models. The balance between experimental and computational method development will depend on the candidates’ profiles. Start date is expected to be 1 June 2026 or as
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-Based Learning approach, the position also includes supervision of project groups and individual students. As part of the appointment, you are also expected to complete the university’s pedagogical
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disciplines relevant to the position include: Mechanical Engineering Operations Management Supply Chain Management Candidates are expected to teach and supervise students at all levels, including PhD students
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processing, and machine learning techniques is considered an advantage. We are looking for a motivated, proactive, and curious PhD candidate that enjoys working across disciplines and contributing to a shared
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) is supported. Please refer to http://mbg.au.dk/ for further information about The Department of Molecular Biology and Genetics and to https://nat.au.dk/ and http://www.au.dk/ for information
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to afforestation. Postdoc projects Postdoctoral position 1 will particularly focus on eddy covariance and auto chamber measurements of CO2 and other GHGs. Main tasks include: Establish and maintain two new eddy
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specimens is particularly strong on Danish and arctic (Greenland) material but also contains material from all over the world. To read more about the botanical collections, see here: https
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and recent projects: https://isim.ku.dk/research/cohep/?pure=en/persons/54263 https://isim.ku.dk/research/cohep/ https://pubmed.ncbi.nlm.nih.gov/?term=jens+bukh&sort=date Profile We are looking for a
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Department of Biology Faculty of Science University of Copenhagen The Kresten Lindorff-Larsen group is looking for a PhD student in computational biophysics and machine learning studies
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possible thereafter. Our research At TreeSense, we use remote sensing and machine learning to develop new means to characterize and quantify global woody vegetation dynamics, both inside and outside