51 assistant-professor-computer-science-data-"https:"-"https:"-"https:"-"UCL" Postdoctoral positions at Aarhus University in Denmark
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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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. Qualifications Applicants must hold a PhD or equivalent qualifications in a relevant field, such as Child–Computer Interaction, Human–Computer Interaction, Learning Sciences, Educational Technology, Computer Science
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computer graphics, or human vision and attention. The posts require research skills in the design of studies, use of methods, research prototyping and data analysis, and you should have documented experience
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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
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. The project includes collaboration with leading international experts in proteomics and dermatology and provides access to modern research facilities. The principal investigator is Assistant Professor Xiang
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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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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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at the Department of Electrical and Computer Engineering, Aarhus University, where we are advancing communication-efficient and distributed foundation model inference across the computing continuum
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feature annotation using Metaboscape and other platforms. Collaborating with the Bioinformatics Core Facility, directed by Associate Professor Per Qvist, and other computational biologists to exemplify
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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