82 computer-programmer-"https:"-"Inserm"-"FEMTO-ST"-"https:" positions at Technical University of Munich
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degree or PhD in Computer Science, Electrical Engineering, Control Systems, or a closely related field. Experience in reachability analysis, formal verification/model checking, control theory, or related
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We are seeking an outstanding candidate for a Postdoctoral position in the field of robot motion and control algorithms for soft material handling, starting immediately. We are looking for a highly qualified and motivated individual with a strong academic background in robotics and a keen...
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the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission analysis, and infrared thermography
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of Orthopaedics and Sports Orthopaedics and the Institute for AI and Informatics in Medicine. We work at the intersection of artificial intelligence, medical imaging, and clinical practice, developing methods
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Germany for more than 12 months in the 36 months immediately before the recruitment date. Nationality: Open to all nationalities. Doctoral Programme Enrolment: You must be willing to enrol in a doctoral
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, investigates how children and adults actively seek, select, and evaluate information to learn about the world. The lab combines behavioral, computational, and cross-cultural approaches to study curiosity
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accessible to users from science and industry Your qualifications: ■ Master’s or equivalent graduate degree in computer science, artificial intelligence, machine learning, mathematics, statistics, data science
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/d) in Energy Informatics, specifically for a DFG project in wind power forecasting using machine learning. You are passionate about applying cutting-edge information technology to solve the energy and
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31.07.2023, Wissenschaftliches Personal Within the Joint Academy for Doctoral Studies (JADS) program of Technical University of Munich and Imperial College London, the Professorship of Energy
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, static user representations, and data sparsity. While deep learning models offer improvements, they often come with high computational costs and require frequent retraining, which limits their scalability