171 parallel-and-distributed-computing-"UNIS"-"Meta" positions at Technical University of Munich
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on the Part Scale. The project is jointly supervised by Prof. Dr. Ralf Jänicke (Institute of Applied Mechanics, Technische Universität Braunschweig) and PD Dr. Stefan Kollmannsberger (Chair of Computational
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representations. • Computational efficiency: Designing adaptive and physics-aware strategies (e.g., optimized residual selection, physics-based zooming) for real-time inference. • Practical usability: Developing
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representations. • Computational efficiency: Designing adaptive and physics-aware strategies (e.g., optimized residual selection, physics-based zooming) for real-time inference. • Practical usability: Developing
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25.08.2025, Wissenschaftliches Personal The Chair of Architectural Informatics at the Technical University of Munich is looking for a research associate (m/w/d) for the research in the frame
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13.10.2025, Wissenschaftliches Personal We are seeking a Project Manager and Data Scientist (f/m/d) to start at our Weihenstephan/Freising location, starting February 2, 2026, for an initial period of two years. The Technical University of Munich (TUM), with its approximately 50,000 students, is...
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and Master’s students in Informatics and Data Science. Supervise Bachelor’s and Master’s theses. We Offer Practice-oriented research projects with leading academic and industry partners (like Google
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of targeted therapies. Analyzing high-dimensional single-cell data has its own statistical and computational challenges, and standard tools often cannot be applied. The purpose of the position and goal
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22.10.2020, Wissenschaftliches Personal PhD and PostDoc Positions in Visual Computing & Artificial Intelligence: we are looking for highly-motivated PhD students and PostDocs at the intersection
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development program (self & time management, communication skills, team development, burnout prevention, structured research work) Support for post-academic career: test field and office space for startups, job
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, static user representations, and data sparsity. While deep learning models offer improvements, they often come with high computational costs and require frequent retraining, which limits their scalability