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optimization layers Increase inference efficiency (e.g., GPU acceleration) and assess the applicability domain of learned algorithms Publish and present your results in peer-reviewed journals and at
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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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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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Summary/ Department Summary: The GPU is a procedural unit running 5 days a week for both ambulatory and inpatients. The GPU performs a wide range of non-sterile procedures: examples include; therapeutic
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decays and related physics object performance studies , development of the real-time analysis (RTA) in particular with ML/AI reconstruction on hybrid GPU/FPGA architecture for the electromagnetic
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application workflows across CPU/GPU clusters, parallel storage, and hybrid cloud platforms. Responsibilities include configuring and optimizing large-scale Linux environments, implementing job scheduling and
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-atmosphere dynamics. We will build an AI-enabled modeling system that couples a GPU-optimized ocean model with a biogeochemical module and AI-based, kilometer-scale atmospheric forecasts. This system will
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Specific Integrated Circuits (ASIC), Processors (CPU, GPU, VPU and accelerators), Field Programmable Gate Arrays (FPGA) and System-on-Chips (SoCs), as well as Intellectual Property (IP) Core developments for
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part of the core PLI team, which includes top-tier faculty, research fellows, scientists, software engineers, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300
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package, including health and life insurance, generous paid leave and retirement programs. To learn more about USC benefits, access the "Working at USC" section on the Applicant Portal at https