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allowance. Generous travel, equipment, and publication funds. Access to NYUAD’s world-class research facilities, including a high-performance computing (HPC) cluster with ~30,000 cores and 34 GPU nodes. Start
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~30,000 computing cores and 34 GPU nodes. The position may start as early as September 2025. Applications will be accepted until the position is filled. To be considered, all applicants must submit via
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Qualifications: Experience in real-time simulation hardware like Opal-RT and RTDS. Experience with software development. Experience with use of GPUs, multi-core CPUs, advanced computing (e.g., QPUs). Excellent
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parallel/GPU computing. Job Duties Job Duty Doing research problems in the area of mathematical foundations of data science and machine learning. The postdoc will assist with ongoing research projects
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. This support includes access to a Titan Krios and Tundra TEMs, fast network interconnects, all-flash network storage, high core density CPU servers, and AI-optimized GPUs. The position is for 2-4 years depending
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will have access to a 70,000-core Infiniband Cluster (Jubail) dedicated to the science division, several GPU-based clusters at NYUAD, and other supercomputer facilities through the CASS network. NYUAD
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developing and implementing very large deep learning models. Familiarity with high performance computing environments (e.g., HPC clusters, GPUs, Cloud resources) and managing Linux based hardware systems
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conferences. Qualifications: PhD in computer science with file systems, GPU architecture experience. Proven ability to articulate research work and findings in peer-reviewed proceedings. Knowledge of systems
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training NLP/deep learning models on GPUs (with framework such as PyTorch, tensorflow) Demonstrated experience with NLP state-of-the-art models (Deepseek, Llama, Mistral, GPT-4, BERT etc) Demonstrated
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
of data scientists/clinicians and working with unique datasets from multiple academic medical centers (e.g. UNC, UCSF, Mayo Clinic, Memorial Sloan Kettering, etc). Lab dedicated GPU workstations/servers and