148 parallel-and-distributed-computing-"Meta"-"Meta" positions at Technical University of Munich
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31.07.2023, Wissenschaftliches Personal Within the Joint Academy for Doctoral Studies (JADS) program of Technical University of Munich and Imperial College London, the Professorship of Energy
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scientific work on design automation for quantum computers and develop methods and software tools dedicated to the design and realization of quantum algorithms/circuits. One of the main challenges in
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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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accessible to users from science and industry Your qualifications: ■ Master’s or equivalent graduate degree in computer science, artificial intelligence, machine learning, mathematics, statistics, data science
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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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leading international journals and conferences • Literature research • Scientific publishing Your qualifications: • Completed academic university degree (university diploma / M.Sc.) in Computer
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insights into the dynamic distribution patterns of human tissue resident T helper cells across space and time. Topic: Dissecting the body-wide spatio-temporal organisation of human resident T helper cells T
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systems coordinate distributed renewable generation like solar and wind, flexible loads like heat pumps and electric vehicles, and distributed energy storage like stationary batteries and hydrogen storage
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computer science with very good results - Interest on topics around the area of distributed systems and data management - Basic knowledge in distributed systems and graph algorithms is desired - Hand-on experience
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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