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- Wrocław University of Science and Technology
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Description The National Centre for Nuclear Research opens the competition for Post-doc position in condensed matter physics/ computational materials science/ machine learning Localization: National Centre
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navigation algorithms, particularly motion planning methods using machine learning techniques. The research will apply deep reinforcement learning algorithms to develop robot local path planners. Testing
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scientific research in the areas of machine learning and pattern classification; 2) publishing research results and participating in scientific conferences; 3) conducting teaching classes and preparing
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scientific research in the area of computer networks, machine learning and optimization; 2) publishing research results and participating in scientific conferences; 3) conducting teaching classes and preparing
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Skills/Qualifications The candidate must possess a Ph.D. degree in robotics or related disciplines, such as computer science. Experience should be related to perception and machine learning, with a focus
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requirements: experience in conducting research using machine learning techniques, supported by a body of research evidence through publications in international journals and conference proceedings; experience
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computer science (programming, image processing, machine learning), operation of engineering design programs and circuit and field simulation software. Requirements Research FieldEngineering » Electrical
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» InformaticsEducation LevelPhD or equivalent Skills/Qualifications 1) conducting scientific research in the field of image processing and machine learning; 2) publishing research results and participation in scientific
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the Institute of Robotics and Machine Intelligence at Poznan University of Technology. We offer full-time position with a fixed term contract. The PostDoc will join our team in learnINg versaTile lEgged
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tomography. Advanced image analysis tools, including machine learning (ML)-based tools, will be used to analyse the resulting datasets. Notably, we propose developing quasi-live and offline image analysis