52 channel-coding-electrical-engineering PhD positions at Technical University of Munich
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08.09.2021, Wissenschaftliches Personal The Professorship of Machine Learning at the Department of Electrical and Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13
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science, physical chemistry, materials science, or materials engineering, or a closely related field • Experience with cellulose and its processing technologies is an advantage • Strong willingness
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the BMBF. ▪ You will contribute to the daily operations of our new experimental laboratory at the Center for Quantum Engineering (ZQE – www.zqe.tum.de). ▪ You will supervise and mentor Bachelor’s and
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multidisciplinary environment at the intersection of cellular biology, toxicology, and epidemiology. As part of our team, you will collaborate with leading experts in environmental health, stem cell technology, and
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of seismic methods and numerical simulations, Good PC and programming skills (e.g., with Python, MATLAB), Experience with measurement techniques and field measurements using sensor technology (ideally using
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bring together different disciplines and perspectives to foster the sustainable management of mountain forests in the Alps. The current position will be part of the Center for Forest Management in the Alps, and
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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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engineering. There is no need for previous knowledge in the described fields but a strong motivation to learn and push the boundaries of our current knowledge further. We are looking for an individual with a
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of mountain forests in the Alps. The current position will be part of the Centre for Forest Management in the Alps, and will work in the subproject 4, remote sensing of mountain forest dynamics (Prof. Dr
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degree in a technical field (mechanical engineering, mechatronics, robotics, electrical engineering, computer science, etc.) -Know-How from lectures in robotics (e.g. environment perception, path and