65 molecular-modeling-or-molecular-dynamic-simulation-"Prof" PhD positions at Technical University of Munich
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Master’s students, supporting their academic and research development. ▪ You will be part of a dynamic international team dedicated to advancing quantum technologies and computing with superconducting qubits
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Biotechnologie und Nachhaltigkeit Prof. Dr. Marc Ledendecker Email: marc.ledendecker@tum.de Web: www.ledendecker-research.com The position is suitable for disabled persons. Disabled applicants will be given
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with the research group “Science & Technology Policy” of Prof. Dr. Ruth Müller The TUM Department of Science, Technology and Society announces an open PhD Position in STS at the Technical University
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06.06.2022, Wissenschaftliches Personal Join the team of Prof. Karen Alim at the TUM Campus Garching to investigate how blood vessels self-organize their network to reach homogeneity in blood flow
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Data Science in Earth Observation Prof. Dr. Xiaoxiang Zhu Arcisstraße 21, 80333 München, Germany Tel. + 49 89 289 22659 xiaoxiang.zhu@tum.de https://www.asg.ed.tum.de/sipeo/ The position is suitable
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of Munich Data Science in Earth Observation Prof. Dr. Xiaoxiang Zhu Arcisstraße 21, 80333 München, Germany Tel. + 49 89 289 22659 xiaoxiang.zhu@tum.de https://www.asg.ed.tum.de/sipeo/ The position is suitable
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systems. Remuneration is 100% TVL E13 according to the German public sector rates A PhD Position is available at the Chair of Algorithms and Complexity. The PhD candidate is supervised by Prof. Harald Räcke
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uncertainty from environment perception and your own state estimation, and then integrating it into a newly developed trajectory and behavior planner. The goal is to enable safe, reliable and highly dynamic
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privacy-preserved fashion. Research topics include, but not limited to, i) handling distributed DL models with data heterogeneity including non i.i.d, and domain shifts, ii) developing explainability and
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privacy-preserved fashion. Research topics include, but not limited to, i) handling distributed DL models with data heterogeneity including non i.i.d, and domain shifts, ii) developing explainability and