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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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-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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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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, PyTorch/TensorFlow) experience working with cluster/GPU computing resources is desirable excellent verbal and written communication skills and the ability to work effectively in a collaborative team
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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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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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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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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