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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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physical sciences, engineering, advanced microscopy techniques, and DNA nanotechnology. Biochemists, synthetic biologists, bioengineers, chemical biologists, chemists, or candidates with a computational
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://mediatum.ub.tum.de/doc/1696192/aab7jokzk7x4paq7m2y9pa2p6.Wetzlinger-2022-NAHS.pdf Job Specifications For PhD applicants: Excellent Master’s degree (or equivalent) in computer science, engineering, or related
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control and strong interest to further develop their skills. The selected applicants are expected to have: • Masters or PhD-level degree in Robotics, Mechatronics, Computer Science or closely related fields
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Sciences (Physik), Technische Universität München Startdatum: Oktober 2025 - ENGLISH: PostDoc and PhD positions in Quantum Simulation Theory, Positions: 2-3 years (PostDoc) & 3-4 years (PhD), Location
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. The team is located within the School of Computation, Information and Technology at the Technical University of Munich, an internationally renowned university with excellent connections to leading research
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Job Offer from June 04, 2025 The Fritz Haber Institute of the Max Planck Society for the Advancement of Science is one of the most internationally renowned research institutes. More than 400
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on the Bildungscampus Heilbronn (Heilbronn Education Campus). TUM Campus Heilbronn focuses on the areas of managing digital transformation, family businesses, and computer science. Requirements - Master’s degree in
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observations, focusing on indoor environments. Qualifications: • Applicants for a PhD must hold a Master’s degree in computer science or equivalent • Fluent written and spoken English skills • Proficient C
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. Requirements: Completed university degree in computer science or applied mathematics, remote sensing, geophysics, physics, or related areas Expertise in computer vision and/or machine learning (deep learning