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scientists and engineers are accustomed to. Moreover, the vast majority of the performance associated with these reduced precision formats resides on special hardware units such as tensor cores on NVIDIA GPUs
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GPU utilization and allocate computing resources efficiently across users. c) create and manage user accounts for faculty and students; troubleshoot user issues; and design and deliver
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to support computations on GPU hardware with various types of Finite Element methods. This work is embedded in a research project considering structure preserving Finite Element methods for multiphase flows
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of today’s heterogeneous hardware (multicore CPUs, GPUs, SmartNICs, disaggregated datacenters). We explore: SmartNICs & P4 switches for offloading intelligence from hosts Device-to-device communication
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. Training LLMs, large-scale deep learning systems, and/or large foundation models using GPU/TPU parallelization while setting up the environment/system network under various constraints, such as limited
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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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modules, and monitor training progress. Display performance metrics (e.g., inference time, GPU utilization, throughput, ROI impact) in real time. System Integration Work with the research team to connect AI
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, scientific computing, etc). Strong scientific computing background, with experience of different architectures (e.g. CPUs/GPUs) and their use in high-performance computing through shared or distributed
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tasks. Application Instructions To apply, please submit your resume with your GPA clearly listed at the top. Applicants should also include 2–3 professional or academic references. What We Provide Hands
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your resume with your GPA clearly listed at the top. Applicants should also include 2–3 professional or academic references. What We Provide Hands-on experience in a dynamic and immersive research