71 cloud-computing-"https:" "https:" "https:" "https:" "https:" "St" scholarships at Technical University of Munich
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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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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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embedded into the quantum technology network formed by WMI, the Excellence Cluster MCQST (www.mcqst.de), the TU München (www.tum.de), Munich Quantum Valley (https://www.munich-quantum-valley.de/), and many
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part of an international team Desired Qualifications Familiarity with cloud computing platforms and large-scale data processing German language skills Publications in computer vision or machine learning
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04.02.2026, Academic staff The successful candidates will be part of the Munich Climate Center and the Earth System Modelling group at TUM (https://www.asg.ed.tum.de/esm/home/) and will be closely
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for an interdisciplinary “bridge-builder” who strives for Scientific Excellence and Real-World Purpose. ● Background: HCI (Human-Computer Interaction), Computer Science, Ethnography, Sociology, or a related
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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
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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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. 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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/264ffa19ca70e3ec41032fe6a4802932b5eda4e6.pdf https://ieeexplore.ieee.org/document/10068193 Job Specifications For PhD applicants: Excellent Master’s degree (or equivalent) in computer science, engineering, or related disciplines (typically