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Field
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-scale distributed geospatial workflows that require GPU-based high-performance computing (HPC) across multiple network domains. Major Duties and Responsibilities: RSG is seeking a senior researcher who
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Workshops (INFOCOM WKSHPS), 2021, pp. 1–6. [4] W. Gao, Q. Hu, Z. Ye, P. Sun, X. Wang, Y. Luo, T. Zhang, and Y. Wen, “Deep learning workload scheduling in gpu datacenters: Taxonomy, challenges and vision
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them. Research Computing & AI Enablement - Work with the Architecture team to build scalable HPC and GPU-enabled environments on IaaS cloud sites along with other specialized hosted solutions. Work
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with OFDM modulation required. Skills Programming skills in MATLAB and or Python required, experience with wireless testbeds desirable, some familiarity with GPU programming desirable (to support
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the next generation of AI leaders, mentor students on groundbreaking research projects with access to state-of-the-art GPU hardware, forge partnerships with industry and academia, and contribute to AI
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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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environments. Experience with parallel computing environments, HPC in a Linux environment. Experience with surrogate modeling. Experience with data analytics techniques. Familiarity with C++ and GPU programming
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). Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications Experience with multi-GPU model training and large-scale inference. Familiarity with modern AI
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/ computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not limited to deep learning Experience utilising GPU
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, forward-looking, and varied research fields and projects, with numerous development opportunities Modern hardware and infrastructure at the workplace, from compute and GPU servers to supercomputers