171 computational-physics-"https:"-"https:"-"https:"-"https:"-"Tilburg-University" Postdoctoral positions in Denmark
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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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. 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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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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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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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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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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, this position offers a unique opportunity. You will work with real robots, real sensors, and real physical interaction problems, contributing directly to the development of autonomous mining systems capable
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-time political science program, Engage in knowledge exchange activities with the wider society, Engage in and contribute to a good working environment at the section and the department level. For
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advice, and education. We offer professional laboratories, greenhouses, semi-field, and field-scale research facilities, advanced computing capacities as well as an extensive national and international
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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