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or comparable degree in physics, biology, bioengineering, material engineering or a related discipline. You have experimental experience in cell/tissue culture, microfluidics, or a related discipline. You enjoy
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University of Munich (TUM), you will be submitting personal data. Please refer to our data protection information in accordance with Art. 13 of the General Data Protection Regulation (DSGVO) http://go.tum.de
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design and impact analysis, resource allocation, growth and welfare evaluation, technology diffusion and firm level innovation. leading to an internationally competitive PhD degree and internationally peer
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the course of the application process pursuant to Art. 13 of the General Data Protection Regulation of the European Union (GDPR) at https://portal.mytum.de/kompass/datenschutz/Bewerbung/. By submitting your
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research project, and the training programme is available on the RTG webpage (https:// www.uni-goettingen.de/rtg2906). Applications are due by 15.01.2026. We ask you to submit your written application as a
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or see http://www.dpz.eu. For more information about the Leibniz Association see www.leibniz-gemeinschaft.de
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Bayesian neural networks. Excellent analytical, technical, and problem-solving skills Excellent programming skills in Python and PyTorch including fundamental software engineering principles and machine
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including fundamental software engineering principles and machine learning design patterns. Be highly motivated and a team player with excellent communication and presentation skills, including experience in
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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 systems with solid
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Management (SLUSE) IPB: Natural Resources and Environmental Sciences (NRES) UPM: Environmental Biotechnology/Environmental Engineering/Environmental System and Modeling UGM: Planning and Management of Coastal