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towards a sustainable future. Subject description This project focuses on the development of GPU-accelerated, high-fidelity thermal runaway simulation models for lithium-ion battery cells, modules, packs
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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port scientific applications to maximize performance across CPU, GPU, memory, storage, and I/O. Contribute technical expertise to faculty projects through the RCC Consultant Partnership Program and other
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computational modelling of additive manufacturing and develop high-performance GPU-based CFD solvers. Qualifications • With PhD degree • Strong research experience in developing GPU-based
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scalable compute solutions integrating CPU/GPU resources, high-speed interconnects, parallel storage, scheduling, and supporting infrastructure. Establish technical standards, architectures, and engineering
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find and assess the most promising habitable exoplanets around solar-like magnetically active stars? Join us to build next‑generation GPU‑accelerated models of stellar dynamos and connect them
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skills include the following: Applicants must demonstrate knowledge of Linux-based computing, high-performance computing, GPU-enabled environments, distributed or cluster-based systems, and AI
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, workstation, remote desktop, GPU, driver, performance, identity, storage, and network-related issues, escalating where deeper platform or infrastructure support is required. Integrate Linux desktops and
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project GPUs (B200s and cloud credits) and GPUs from the Department of Computing and the College of Engineering (A100s and H200s) The opportunity to continue your career at a world-leading institution and
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for operating and developing a research infrastructure comprising CPU- and GPU-based HPC systems, petabyte-scale storage, scientific web services, secure environments for sensitive data, and emerging AI-related