161 computer-programmer-"St"-"FEMTO-ST"-"UCL"-"St" positions at Technical University of Munich
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prestigious Humboldt Research Fellowships for the Logistics and Supply Chain Management research group at TUM School of Management. With the scouting program, the foundation aims to attract highly talented
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the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission analysis, and infrared thermography
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representations. • Computational efficiency: Designing adaptive and physics-aware strategies (e.g., optimized residual selection, physics-based zooming) for real-time inference. • Practical usability: Developing
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of Orthopaedics and Sports Orthopaedics and the Institute for AI and Informatics in Medicine. We work at the intersection of artificial intelligence, medical imaging, and clinical practice, developing methods
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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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representations. • Computational efficiency: Designing adaptive and physics-aware strategies (e.g., optimized residual selection, physics-based zooming) for real-time inference. • Practical usability: Developing
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academic assignments at the chair What we look for in you Completed master’s degree in computer science, transportation, or related engineering fields Solid background in generative AI, machine learning, and
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Profile The ideal applicant has a strong background in bioinformatics or computational chemistry, as well as data analysis and solid English-language skills. Experience with programming is highly
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(https://soilsystems.net/ ), a Priority Programme (SPP 2322) funded by the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation). Within SoilSystems, scientists from different disciplines from
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://mediatum.ub.tum.de/doc/1687691/ltbbjgwihiht96fqfeblxdyj7.ITSC22_CommonOcean.pdf Job Specifications For PhD applicants: Excellent Master’s degree (or equivalent) in computer science, engineering, or related disciplines