142 computational-physics "https:" "https:" "https:" "https:" "IFM" positions at Aarhus University
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We are seeking applicants for a 2-year postdoc in Ultrafast X-ray probes of Quantum Materials to join us at the Department of Physics and Astronomy. Starting Date and Period The position is for 2
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-based approaches to documenting, describing, and analysing language and social interaction, as well as on experimental and computational modelling of human cognition and behaviour. We are seeking
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Technical Sciences Tenure Track Aarhus University offers talented scientists from around the world attractive career perspectives via the Technical Sciences Tenure Track Programme. Highly qualified
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Department of Electrical and Computer Engineering (ECE), Aarhus University (AU) invites applications for a position as Tenure Track Assistant Professor/Associate Professor in electronics
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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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present at the interdisciplinary nanoscience center at Aarhus University, where the lab is physically located. Your profile We are looking for a highly creative, collaborative and experimentally skilled
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candidates will be involved in materials crystallography research in collaboration with other members of the Iversen group. The candidates must have a PhD in chemistry, crystallography, physics, materials
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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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The Department of Biology at Aarhus University aims to advance and strengthen the Departments research and education program in ecophysiology and ecology. Therefore, we invite applications for 1-2
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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