48 post-doc-soil-structure-interaction Postdoctoral positions at Technical University of Munich in Germany
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our enthusiastic and collaborative group spirit. Post-doc: The applicant is expected to have a solid publication record in theoretical CS. Experience with biological applications, robotics applications
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- Post Doc Applicants Only: academic track record with publications at top-tier venues in computer vision, graphics, or machine learning (CVPR, ECCV/ICCV, Siggraph, Siggraph Asia, NeurIPS, ICML, ICLR) How
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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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of Interfaces, TUM) and Prof. Willi Auwärter (Molecular Engineering at Functional Interfaces, TUM). Project: We aim to use the unique optical, photophysical, electrochemical, and structural properties
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field of additive manufacturing of metallic structures. The main focus is on developing and characterizing metallic high-performance materials for/through additive technologies by means of experiments and
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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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, with the option to extend. TUM’s benefits for employees (https://www.tum.de/en/about-tum/working-at-tum/services-for-employees/ ). Application If you are interested in joining us, please send us your
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the representation of women in science. Accordingly, we strongly encourage applications from qualified female candidates. How to apply Please send your application materials to office.nen@xcit.tum.de, with email
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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