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related discipline. Strong expertise in medical imaging and/or machine learning. Excellent programming and research skills. Interest in translational research and interdisciplinary collaboration with
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, imaging). • Solid foundations in signal processing and statistics. • Experience with machine learning for regression (e.g., tree-based methods, neural networks) • Hands-on experimental skills: ability and
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to joint research activities, publications, and surveys. Requirements PhD degree (or near completion) in robotics, control, machine learning, or a related field; Strong publication record demonstrating
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management skills • Experience with qualitative or mixed-methods research • Familiarity with AI, machine learning, neurotechnology, or robotics research contexts • Interest in science policy, governance
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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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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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-XRF, Raman, FTIR in reflection mode) to enable multimodal data fusion and automated material characterization. • Apply and further develop machine-learning and statistical models (e.g. PCA, SAM
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social or behavioral sciences (incl. human-computer interactions with relevant experience). Applicants must demonstrate experience in experimental work with human participants; possess versatile
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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 academic record, including
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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