146 distributed-computing-"St"-"University-of-St"-"St" positions at Technical University of Munich in Germany
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computer aided methods. Qualifications and Experience • Outstanding academic degree in materials science, metallurgy, metal physics or similar degree • Excellent doctorate with focus on computational
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methods, machine learning algorithms, and prototypical systems controlling complex energy systems like buildings, electricity distribution grids and thermal systems for a sustainable future. These systems
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of Orthopaedics and Sports Orthopaedics and the Institute for AI and Informatics in Medicine. We work at the intersection of artificial intelligence, medical imaging, and clinical practice, developing methods
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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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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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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
of Munich (TUM), Campus Heilbronn. We are looking for exceptional candidates who are interested in pursuing a PhD in either theoretical computer science or graph and network visualization. We seek PhD
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finite elements) as well as alternative discretization methods (e.g., Lattice Boltzmann Methods), and high-performance computing. A selection of possible research areas can be found on our website: https
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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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distribution analysis with collaborators at the University of Ljubljana in Slovenia. Data analysis and manuscript preparation. Presenting results at international conferences. Training Master’s and Bachelor's
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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