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to gather requirements, manage expectations, and balance competing priorities. Experience supporting high-performance or GPU-enabled scientific computing environments, including NVIDIA/CUDA, performance
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establish a research profile. Develop and execute innovative research projects. Develop, train, and evaluate modern machine-learning models on GPU/HPC infrastructure. Integrate AI methods with scientific
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Center for Generative AI hosts the largest academic GPU cluster in the world and includes one of our faculty members on its team. The Texas Advanced Computing Center provides world-class AI compute
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,maintains, and supports imaging analysis environment, including a GPU-equipped workstation for quantitative CT (qCT) analyses such as lung segmentation, and installs, configures, and manages related analysis
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engineering or digital twins. - Experience with scientific visualization tools and methods. - Experience with NVIDIA CUDA or GPU computing. - Experience with Docker or Kubernetes containerization and
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simulation, Cosserat rod theory to support hands-on class activities and development of NVIDIA GPU-based course modules. • Availability to schedule, coordinate, and oversee student visits to operating rooms
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Karel. He's an AI agent we built, and he writes like a monk keeping vigil over a datacenter. Every morning Karel files a report: which pods are healthy, which GPUs sit idle, which tickets are aging, where
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architectures. Emphasis will be placed on heterogeneous computing and the optimal use of CPU and GPU resources to maximize model performance, scalability, and portability. A particular focus of the position will
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, over 20 dexterous robotic hands and 12 robot arms for real-world data collection and teleoperation, high-resolution tactile skins, and a dedicated GPU cluster with eight NVIDIA H200 GPUs alongside access
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efficient inference. Experience with GPU computing and high-performance or distributed computing environments. Exposure to planning, scheduling, workflow engines, or reasoning systems, and to scientific