75 computer-security "https:" "https:" "https:" "https:" "https:" "Dr" "Robert Gordon University" PhD positions at Technical University of Munich
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. Classical cryptographic techniques face inherent limitations, especially regarding future threats from quantum computers or AI-driven adversarial strategies. Physical layer security offers a promising
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in cancers of unknown primary (CUP). Your Role You will join Subproject 3 (Model Alignment and Optimization), led by PD Dr. Keno Bressem (https://scholar.google.com/citations?user=wIEgwbkAAAAJ&hl=en
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of two references to apl. Prof. Dr. Friedrich Esch (recruitment.pc@ch.tum.de). Only complete applications will receive full consideration. The position is open until 30.06.2026 or until filled. Further
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knowledge of the German language besides English. If interested, please send your full application to the email adress provided below. At the Mechanics & High Performance Computing Group, there is an open
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06.02.2026, Academic staff The Laboratory for Ethics of Artificial Intelligence and Neuroscience at the Technical University of Munich (TUM), headed by Prof. Dr. Marcello Ienca, is seeking
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. (maria.vrachioli@tum.de) or Dr. Fabian Frick (fabian.frick@tum.de). As part of the Excellence Initiative of the German federal and state governments, TUM has been pursuing the strategic goal of substantially
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position, please contact Maria Vrachioli, Ph.D. (maria.vrachioli@tum.de) or Dr. Fabian Frick (fabian.frick@tum.de). As part of the Excellence Initiative of the German federal and state governments, TUM has
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Technical University of Munich School of Computation, Information and Technology Chair of Theoretical Information Technology Theresienstrasse 90, 80333 Munich https://www.ce.cit.tum.de/en/lti/team/boche
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molecular level. To yield new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at leveraging graph-theoretic approaches to analyze and
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tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in the form of graphs to analyze and predict food-effector systems. Key