171 computational-physics-"https:"-"https:"-"https:"-"https:" Postdoctoral positions in Denmark
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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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methods, design interventions, and digital/computational methods to engage users, publics, and partners in exploring dilemmas, controversies, and paths to sustainable and desirable futures. We strive
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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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questions about the position, you are more than welcome to contact us. You will find contact persons at the bottom of the jobpost. Further information Read more about our recruitment process here
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candidates with a degree in physics, chemistry or materials science. For Topic 1-3, candidates must have documented skills in atomic-resolution electron microscopy, microfabricated devices, 2D materials
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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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. Applicants must have: A relevant PhD degree (Wireless Communications, or a related field) Demonstrated research experience in physical layer, multi-antenna technologies, medium access control of wireless
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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