276 computational-physics "https:" "https:" "https:" "Tilburg University" Postdoctoral positions in Denmark
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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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colleagues and a formalized teaching and training program focusing on methodological as well as transferable skills, including dissemination, collaboration, and work planning CPPEM as a whole spans research
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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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University, where the lab is physically located. Your profile We are looking for a highly creative, collaborative and experimentally skilled candidate with (or close to finishing) a Ph.D. in molecular biology
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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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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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(DTU) welcomes aspiring candidates to expand the field of two-state water thermodynamics. This PostDoc project is funded by the Villum Experiment Programme “Can the two-state theory of water
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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