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-following inverters. Implementing and optimizing scalable algorithms for transient and stability analyses on HPC architectures (CPU, GPU, hybrid). Enhancing the numerical robustness and efficiency of existing
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skills in Python and/or R; experience with Linux/HPC environments is an advantage Experience with genomic data analysis, high-performance computing, GPU programming, or software development is a plus
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HPC centers. Dresden, the capital of the State of Saxony, is a city with a beautiful historical city center and offers a high standard of living with high ratings in housing, safety, and healthcare. TUD
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. The chair hosts its own computer cluster and has full access to the high-performance computing infrastructure at ZIH Dresden, one of Germany’s leading HPC centers. Dresden, the capital of the State of Saxony
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to state-of-the-art HPC and ML facilities via the de.NBI Cloud and the Jülich Supercomputing Center. The possibility for participation in the Helmholtz Data Science Initiative and Helmholtz AI. Strong
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GPU-capable, parallelized simulation frameworks. Work closely with experts in HPC and power systems to enhance scalability and computational performance. Disseminate your findings through scientific
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topical subject area. Participation in project meetings and conferences. Opportunities for limited-time research stays abroad. Access to state-of-the-art HPC and ML facilities via the de.NBI Cloud and the
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communities, most importantly in helmholtz.ai, the Helmholtz Artificial Intelligence Cooperation Unit: https://www.helmholtz.ai Presenting research results at scientific meetings, conferences, and as scientific
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into concrete tractable problems you can solve. Be equally passionate about rigorous mathematical reasoning and hands-on Python implementation (PyTorch, JAX, …) in HPC environments. Communicate clearly in English
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include experience with high-performance computing (HPC), initial experience with ecosystem models, willingness and ability to actively contribute to interdisciplinary teams, knowledge of climate-relevant