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object interaction (using environments such as MuJoCo, PyBullet, or NVIDIA Isaac Sim) Adaptive feedback control using learned and analytical models Physics-informed machine learning for deformation
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interactive and international environment. Numerous possibilities for further training in the sciences and beyond. Website of the Chair of Plant Systems Biology at TUM: http://sysbiol.wzw.tum.de Application We
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polycephalum arises from the interactions of cell nuclei within the gigantic cell. We are looking for a Post-doc (m/f/d) to start at the TUM this summer or fall. Your Task Physarum polycephalum is renowned
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PhD/Postdoc position in trustworthy data-driven control and networked AI for rehabilitation robotics
of psychology (in human-robot interaction) and communications (in networked control systems). Many of the developed methods are experimentally validated in our multi-robot lab. Your qualifications: We
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Euro / year + benefits). 3D Semantic Scene Understanding: The world around us exists spatially in 3D, and it is crucial to understand real-world scenes in 3D to enable virtual or robotic interactions
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Euro / year + benefits). 3D Semantic Scene Understanding: The world around us exists spatially in 3D, and it is crucial to understand real-world scenes in 3D to enable virtual or robotic interactions
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interactions with such environments. We investigate machine learning approaches to infer semantic understanding of real-world scenes and the objects inside them from visual data, including images and depth/3D
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responsibilities: - implement, develop and extend methods for processing and analyzing single-cell RNAseq and protein profiles (CyTOF) - develop methods for second level analyses e.g. interaction networks