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-informed machine learning for deformation modeling and prediction Integration of perception, planning, and control for robust real-time robotic performance Requirements Ph.D. in Robotics, Mechanical
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these mechanisms, as they reca-pitulate key aspects of embryonic development and tissue morphogenesis in vitro. The group of Prof. Bausch investigates the physical principles underlying these emergent processes and
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organizational levels of the brain – from molecular and cellular processes to complex neuronal networks and behavior. The research group “Behavioral Neuroscience” focuses on decoding the exact mechanisms behind
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writing. Drive the publication of research results in top-tier robotics conferences and journals. Requirements Ph.D. in Robotics, Mechanical Engineering, Electrical Engineering, Computer Engineering, or a
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track record Above-average master’s degree in computer science, electrical/ mechanical engineering, applied mathematics, or a similar engineering-oriented quantitative discipline Advanced software
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related field (e.g. battery technology, automotive engineering, electrical engineering) and have previously completed your master's degree in a technical field, such as mechanical engineering, computer
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, information technology, electrical engineering, physics, mechanical engineering, or a comparable qualification Sound knowledge of mathematics and physics, especially in the fields of linear algebra and numerical
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(sensitivity, stability, selectivity) towards fast- tracking sensing functionality using a variety of electrochemical techniques. • Investigate and validate the sensing mechanism and rate limiting steps and
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the following areas: PhD in mechanical/electrical engineering, robotics, computer science, or a comparable field, Experience in self-reliant managing of research projects (financial and administrative) and