334 computational-physics "https:" "https:" "https:" "https:" "IFM" research jobs in Denmark
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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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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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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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, 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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of Copenhagen can be found here: https://employment.ku.dk/faculty/recruitment-process/ An Equal Opportunity Workplace The University of Copenhagen is committed in its pursuit of academic excellence to equality
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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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understanding of material science and electrical transport physics. Experience with electrical characterization of devices, ideally low-noise characterization of magnetometers. A strong drive to move
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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