842 computer-science-image-processing-"Gustave-Roussy-"-"Gustave-Roussy-" positions in Denmark
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, electrical engineering, etc. Prior experience in (1) image processing, particularly for radiographic and computed tomographic data as well as mesh-type data, and (2) machine learning, particularly deep
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, physics, computer science, applied mathematics, or similar Required competences Strong background in image processing and analysis, especially Deformable image registration and 3D segmentation methods
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. You will set the agenda in optical imaging for health technology and contribute to flagship initiatives that improve patient outcomes through prediction, prevention, early diagnosis, and treatment
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programme in optical imaging for health technology, exploring emerging and enabling technologies and their potential applications in healthcare. This includes integrating novel concepts such as advanced
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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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At the Faculty of Medicine, Department of Health Science and Technology, a position as Assistant Professor in Tongue computer interfacing for computers and roboticsis open for appointment from June
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At The Technical Faculty of IT and Design, Department of Architecture, Design and Media Technology (Copenhagen Campus) a full-time position as Associate professor in in real-time computer graphics
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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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Research Assistant in Physical Computing and Wearables at the Department of Computer Science, Aar...
We invite applications of Electrical Engineers (Ba/Ma) for participating in a research projects for 2 months starting May, 1st, 2026 in the HCC section at the Department of Computer Science. Our
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process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support process and