48 assistant-professor-computer-science-data Postdoctoral positions at Aarhus University
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The Section for Electrical Energy Technology at the Department of Electrical and Computer Engineering (ECE), Aarhus University, is in a phase of rapid growth in both education and research
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. Consequently, your employment will as of that date be with a department. Contact information For further information, please contact: Assistant Professor Emil Laust Kristoffersen, +45 29271306, emillk
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professor in radiobiology, and eleven associate/assistant professors in medical physics or oncology (most of the latter in part-time positions, in combination with a position in the clinic). We currently have
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information For further information, please contact: Professor Torben Heick Jensen, thj@mbg.au.dk, phone +45 60202705 Application procedure Shortlisting is used. This means that after the deadline
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friendly atmosphere of large and interacting communities for e.g. structural biology, molecular cell biology, computational biology, neurobiology, and molecular medicine that encourages lively, open, and
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soon as possible thereafter. The position will be for two years. The postdoc will be supervised by Professor Doug Speed, and based at the Department of Quantitative Genetics and Genomics (QGG) at Aarhus
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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
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as possible hereafter. For more information contact Professor Tenna Riis (tenna.riis@bio.au.dk) or Assist. Prof. Johnna M. Holding (johnna@ecos.au.dk). Post doc 2: Glaciers are melting
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such as multi-level models, target trail emulation, mediation analysis and g-computation Supervision of bachelors’ and masters’ students You will report to Professor Christina C. Dahm. Your competences You
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The Department of Ecoscience at Aarhus University invites applications for two postdoctoral positions to strengthen our research on image recognition, computer vision and deep learning applied