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: 150271094 New Graduate Registered Nurse, Gastroenterology Procedure Unit (GPU) - 30hrs Boston Children's Hospital (BCH) Gastroenterology Procedure Unit (GPU) supports a wide array of anesthesia supported
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configuration of RC systems, including a local HPC cluster, standalone compute and/or GPU systems, Linux workstations and other supporting systems. Set standards for monitoring and maintaining the health 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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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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tuned for thermal transport and fire dynamics. FDS has been scaled up to 10,000 cores on Titan. The aim of this project is to improve code performance for serial, OpenMP, MPI, and potentially GPU
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the next generation of AI leaders, mentor students on groundbreaking research projects with access to state-of-the-art GPU hardware, forge partnerships with industry and academia, and contribute to AI
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, resource requests, and environment management. Desired Requirements: 1. Probabilistic modeling: scVI/scANVI/totalVI for RNA and RNA+protein integration. 2. GPU experience: PyTorch/CUDA for segmentation/model