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in two-phase transport, heat transfer, and fuel cell electrochemistry The ECEC, directed by Prof. Chao-Yang Wang, is a multi-disciplinary research program concentrating on advanced batteries and fuel
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. Experience with heterogeneous and parallel computing technologies, programming models, or accelerators, including CPUs, GPUs, FPGAs, and emerging computing technologies. Familiarity with quantum software
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research skills, evidenced by high-quality publications in top-tier machine learning/AI conferences and/or leading scientific journals. Excellent programming skills and hands-on experience with leading
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CPU and GPU based HPC systems. Exploration of the capabilities of DPU/IPU SmartNICs to support network security isolation, platform level root-of-trust, and secure platform management/partitioning
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institutional clusters. Write robust Linux bash scripts and job submission scripts for SLURM and PBS environments, including multi-node GPU/CPU workflows, monitoring, restart, and post-processing pipelines
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transfer (CHT) or fluidic manifold optimization Experience with machine learning/AI (PyTorch or TensorFlow), reduced-order modeling (ROM), or data assimilation (DA) Experience with GPU programming (CUDA and
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avoidance, autonomous exploration, frontier selection, and localization-aware trajectory planning. Develop high accuracy point cloud registration and mapping workflows. Plan and conduct experiments
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AI, …) and relevant programming frameworks Advanced programming skills in relevant programming languages and contexts (e.g., Python, HPC/GPU programming, big data applications) Strong team spirit and
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relevant programming frameworks Advanced programming skills in relevant programming languages and contexts (e.g., Python, HPC/GPU programming, big data applications) Strong team spirit and experience in
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with SO faculty and scholars in the Department of Astronomy. UA/SO offers a world-class research environment in space science and astrophysics, with strong interdisciplinary programs across