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for safety-critical bilateral teleoperation. The research will leverage a combination of passivity-based control methods and machine learning techniques to enable reliable and robust teleoperation in uncertain
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involve the integration of: Advanced motion planning and control algorithms Multi-modal perception techniques (e.g., vision, tactile, force) Machine learning models for physical behavior prediction and
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institution of TUM Campus Heilbronn that uses data to answer relevant questions and solve real-world problems. It brings together fundamental, methodologically driven research in optimization, machine learning
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methods in AI and machine learning, contributing directly to state-of-the-art research with high industrial relevance. Your Qualifications A strong background and Master's degree in Computer Science, AI
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, Rehabilitation, Biostatistics, Python, R, SQL, Machine Learning, Real-World Data, TUM Hospital The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally
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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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ENGAGE Network at TUM and GIM Robotics. About the ENGAGE Network Mobile working machines (MWM) are critical to industries like construction, mining, and agriculture, and key to Europe’s sustainability and
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Researchers: Ph.D. in Computer Science or Mathematics, ideally with a background in one or more of the following areas: Optimization, Game Theory, Machine Learning Applicants must demonstrate: • An excellent
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data to answer relevant questions and solve real-world problems. It brings together fundamental, methodologically driven research in optimization, machine learning, and artificial intelligence with
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collisions and maximize efficiency through innovative AI-based movement and maneuver planning. For the first time, innovative machine learning concepts, such as “shadow learning”, are being used. Appropriate