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efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow prediction Integration of domain decomposition methods into the learning framework to enable
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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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model in collaboration with partner institutions such as the German Climate Computing Center (DKRZ) and German Weather Service (DWD), including GPU porting. They will perform production runs of ICON and
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program embedded in a large-scale, nationally funded research consortium with access to unique multimodal clinical datasets - State-of-the-art GPU infrastructure for training and fine-tuning large
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E13) up to 5 years International collaboration to build a large radiotherapy dataset Dedicated GPU infrastructure Strong collaborations within TUM’s AI ecosystem High-impact publication potential
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, mathematics or any related field. What we offer State of the art on-site high performance/GPU compute facilities Competitive research in an inspiring, world-class environment A wide range of offers to help you
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containers (Docker/Singularity/Podman/Kubernetes). Experience with Ethernet, InfiniBand, RDMA network technologies. CPU/GPU/memory/RAID/storage/Data Center technologies. Knowledge of current technological
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-end GPUs We will jointly choose tasks based on your expertise and interests. Most important is a strong interest in scientific methods, a solid knowledge foundation (e.g. studying computer science, open
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physics, mathematics or any related field. What we offer State of the art on-site high performance/GPU compute facilities Competitive research in an inspiring, world-class environment A wide range of offers
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Your profile: Preferably a doctoral degree, but MSc are also encouraged to apply Expert knowledge in one or several of the following High Performance Computing GPU computing Array Computing with JAX A