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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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-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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the development of scalable software tools and pipelines, potentially leveraging GPU/FPGA accelerators. Our aim is to build next-generation molecular atlases for chronic diseases and to improve patient
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of visualisation, machine learning / AI, and human-computer interaction Very good programming skills (web-based visualisation, Python, and/or GPU programming) First experiences in the participation in research
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, large-scale medical datasets and high-performance computing infrastructure (including NVIDIA B300 GPUs) Funding for publications, international conferences, and research mobility grants Support from TUM