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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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-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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, 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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approaches, the application of meta learning, and the integration of convex optimization layers Increase inference efficiency (e.g., GPU acceleration) and assess the applicability domain of learned algorithms
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commonly used on Unix systems. Additional languages or experience with libraries for utilizing GPU hardware efficiently, e.g., CUDA, are a plus. Experience in AI programming with, e.g., PyTorch(-DDP
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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
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Germany Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research
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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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-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