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companies from all over the world, especially the USA, the UK, and Germany. Your Profile: Excellent university degree in engineering, chemistry, materials science, physics, electrochemistry or a similar
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networks and their demonstration as proof-of-concept implementation in an experimental 6G testbed. Your qualifications MSc in Computer Science or Electrical Engineering Strong background in networking and
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computer science. It offers automatic grading and feedback of various types of exercises. The tool has been used at dozens of universities around the world (including 5 times at TUM) and graded almost a million
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management, (organizational) psychology, sociology, economics, business informatics, or related subjects, • with a strong passion for entrepreneurship and/or family business research, • with a solid knowledge
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for benchmarking, HPDA/HPC support, and education (e.g. MOOCs, organizing workshops, facilitating community building). Requirements: Completed university degree in computer science or applied mathematics, remote
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: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong knowledge in Machine/Deep Learning with experience in discriminative models
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journals. Close collaboration with team members and colleagues. Essential qualifications: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong
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fundamental knowledge about the handling and capturing of flow behavior in multistage compressors. The collaborative frame with a prestigious industry partner will give insight to future technology requirements
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an additional optional portfolio. Interviews will be held and employment commence as soon as possible after the selection is made. Technische Universität München TUM School of Engineering and Design Dep
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the developed code Publishing the developed approaches in international journals and conferences Requirements Promising applicants have: A master’s degree in Computer Science, Geodesy, or related discipline Very