49 biomedical-signal-processing Postdoctoral positions at Technical University of Munich
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19.07.2022, Wissenschaftliches Personal The Machine Learning and Information Processing group at TUM works in the intersection of machine learning and signal/information processing with a current
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made from magnetoelectric materials, which transduce wireless magnetic powering signals into local electric signals that can be used to stimulate neurons. Our multidisciplinary group works in materials
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via email to applications@mirmi.tum.de quoting “Postdoc Position in Dexterous End-Effectors for 6G Tactile Tele-surgery” in the e-mail subject line. The position will be filled as soon as possible and
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flow within cerebral aneurysms. Arterial geometries are derived from medical scans (e.g., CT) of real patients, which are suitably meshed and processed for numerical treatment using Lattice-Boltzmann
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and Ethics in Medicine, we advance ethical practice and theory in medicine, biomedical technology, and public health, driven by the belief that embedding ethics is essential for shaping the future
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talented individuals passionate about AI, Human-Computer Interaction, Eye-Tracking, and their responsible applications. Ideal candidates will have: • An M.Sc. degree (or equivalent) in Computer Science, Game
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Engineering, Computer Engineering, Computer Science, or a closely related field Strong background in robotics fundamentals: kinematics, dynamics, control, planning Proficiency in programming (C++, Python), and
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University of Munich (TUM). Please view our privacy policy on collecting and processing personal data in the course of the application process pursuant to Art. 13 of the General Data Protection Regulation
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of porphyrins in conjunction with novel linker structures to affect a transformational advance in the molecular design and control of interfacial nanoconstructs with tunable electronic, photonic, biomedical, and
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04.04.2025, Wissenschaftliches Personal The Chair for Computer Architecture and Parallel Systems (CAPS) offers this position as part of the DARE-project funded by the EuroHPC JU bringing together