30 computational-physics-"https:"-"https:"-"https:"-"https:"-"UCL"-"UCL" Postdoctoral positions at Aarhus University in Denmark
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We are seeking applicants for a 2 year-postdoctoral position to join us in the Optomechanics group at the Department of Physics and Astronomy in order to work with nanoguitar optomechanical
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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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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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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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graph algorithms for optimization under physical constraints Applying graph mining and graph data management techniques Designing computational methods for waste heat reuse and green transition goals
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This is a full-time (37 hours/week) on-site role located at Åbogade 34, 8200 Aarhus N, Denmark for a Postdoctoral Fellow at the Department of Computer Science, Aarhus University. The postdoctoral
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level, e.g. in medical physics, physics, biomedical engineering or computer science. It is mandatory that your PhD degree is on a topic relevant for this specific position, e.g. in medical image-based
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decomposed into modular sub-components that can be either process-based models and/or deep learning models. MCL has the flexibility to replace any uncertain process description with a deep learning model
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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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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