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Field
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of predicting electronic, structural, and thermal quantities while leveraging underlying symmetries for computational efficiency. There will be a significant computational component in deploying multi-GPU codes
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Shifting bits: Adaptive numerical precision for GPU software in particle physics and beyond (S3.5-COM-Richmond)
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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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-node GPU training and inference pipelines for foundational models. You'll also develop tools for ingesting, transforming, and integrating large, heterogeneous microscopy image datasets—including writing
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infrastructure, model training, and inference systems. You'll design, develop, and optimize scalable data pipelines and build multi-node GPU training and inference pipelines for foundational models. You'll also
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that combine parallel architectures (i.e., GPUs or accelerator boards, clusters) and numerical algorithms suited to such architectures with the goal of improving the speed of convergence and the stability
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. The role involves the design, implementation, and testing of GPU compute kernels, and associated host code, for the CHR real-time pipeline. Particular challenges include high-throughput beamforming via a
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, networks and GPU resources. The role ensures secure, efficient, and scalable systems to support research and administrative functions. This position work closely with PI, researchers and technical vendors
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and collaborative team of computational scientists, software and AI engineers, and neuroscientists, you’ll have access to high-performance workstations, CPU/GPU clusters, and experimental systems
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fonctionnant sur CPU et GPU, cette thèse de doctorat vise à caractériser la dynamique de ces ondes de choc, leur évolution à long terme et leur signature observationnelle. Cette thèse est financée par une bourse