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experimental data and workflows. The team drives innovation in algorithm design, GPU-accelerated computing, and quantum-ready methodologies applicable to complex scientific problems across the experimental
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Engineers. Serve as liaison with Princeton Research Computing staff on GPU cluster related issues. Professional Development Learn the underlying science, mathematics, statistics, data analysis, and algorithms
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 3 months ago
. 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 balance work and family
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. 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), Horovod, or DeepSpeed, and in
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these resources through a cloud-native Kubernetes environment integrating large-scale CPU and GPU resources, Ceph object storage, BinderHub, Coffea-Casa, Dask, and ServiceX. This platform supports more than 500
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | 13 days ago
approaches for automated medical devices (e.g., physiologic closed-loop controlled devices). Developing multi-spectral computational modeling tools using GPU-based processors to map light propagation
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. Evaluates and selects appropriate foundational models (OpenSource vs. Proprietary) and hosting strategies (Azure AI Foundry, AWS Bedrock, local GPU/TPU), directly influencing the University's cloud spend and
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pipelines for complex decision‑making. Conducting adversarial testing, implementing input sanitization, and contributing to AI‑safety research. Utilizing GPU/TPU resources, mixed‑precision training, and
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, PyTorch) for ML applications, training, evaluation, and deployment of models Use of GPU-based servers and modern IT infrastructure for training and inference Application of classical ML methods (e.g
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samples. Optimize reconstruction algorithms for efficient large-scale 3D imaging, including high-performance and GPU-accelerated computing where appropriate. Design, optimize, and validate a refractive