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grade. You embrace a PhD project on the design, fabrication, and characterization of innovative superconducting circuits for microwave photon detection in the context of a quantum illumination experiment
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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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research into novel sensors and discover their applications. A core tool of the group are diamond quantum sensors for magnetic fields. Your project You will spearhead one major research effort of our lab
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and transcript of records no later than 30th of April 2021 to Ingo Glaser (ingo.glaser@tum.de ). You can find further information here: https://wwwmatthes.in.tum.de/pages/qyey9hn4lw80/Open-PhD-Position
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group, a multinational insurance company. Tasks Your duties will include: Literature research Designing, implementing, and evaluating novel machine learning approaches to detect building attributes from
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researcher in the project: As doctoral researcher in the project your main task is to contribute to achieving its objectives. You are required to conduct qualitative social science research, including
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explicitly encouraged to apply. Disabled applicants will be given preference if their suitability and qualifications are otherwise essentially equal. Data protection notice When applying for a position at the
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submitting your application, you confirm to have read and understood the data protection information provided by TUM. Find out more about us at https://www.bgd.lrg.tum.de/ The position is suitable for disabled
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News Detection Topic / Area: The positions are flexible in terms of research direction within 3D vision with a heavy focus on cutting-edge deep learning-based techniques. We are particularly interested
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: Technical University of Munich Chair of Materials Engineering of Additive Manufacturing Univ.-Prof. Dr. Peter Mayr Boltzmannstraße 15, 85748 Garching b. München Tel. +49 89 289 55341 info@mat.mw.tum.de Find