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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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Your Job: Investigate the potential of novel computing architectures for lattice field theory workloads Contribute to the design and implementation of an open-source lattice field theory framework
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tasks: You will work together with renowned astrophysicists and computer scientists in the DFG-funded “Dynaverse” Excellence Cluster You will invent, implement, and benchmark novel AI tools (reinforcement
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graduate school program (including data science courses, soft skill courses and annual retreats) https://www.hds-lee.de/about/ Further development of your personal strengths, e.g., via a comprehensive
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Science Beamline (P61A/WINE), the lmaging Beamline (P05/IBL), and the Nanofocus Endstation of Micro? and Nanofocus X-ray Scattering Beamline (P03/MINAXS), along with supporting laboratories (https
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Endstation of Micro‑ and Nanofocus X-ray Scattering Beamline (P03/MINAXS), along with supporting laboratories (https://gems.hereon.de). The techniques offered at our diffraction beamline P61A have a 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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edge of energy systems and computational engineering, developing scalable methods to simulate and secure IBR-dominated grids. Your key responsibilities include: Conducting large-scale simulations
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Germany Application Deadline 2 Nov 2025 - 22:59 (UTC) Type of Contract To be defined Job Status Other Offer Starting Date 17 Oct 2025 Is the job funded through the EU Research Framework Programme? Not
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performance computing systems or cloud infrastructure (including GPU-accelerated workloads). Practical experience with modern deep learning frameworks, model serving in production, and building end-to-end data