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: Experience in real-time simulation hardware like Opal-RT and RTDS. Experience with software development. Experience with use of DSPs, FPGAs, advanced computing (e.g., GPUs, QPUs). Excellent written and oral
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supporting new research and engineering using ORNL’s Frontier exascale supercomputer for its dense GPU-based HPC resources to train, deploy models and create large-scale production datasets for high-impact
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supercomputer for its dense GPU-based HPC resources to deploy models and create large-scale production datasets for high-impact sponsor missions. The candidate will be expected to handle sponsor requirements and
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learning algorithms (robustness; security/privacy, e.g., DP or FL; and uncertainty quantification). Experience scaling training/inference on GPU-accelerated HPC systems and collaborating on multi-institution
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supercomputer for its dense GPU-based HPC resources to train large GeoAI models and deploy models and create large-scale production datasets for high-impact sponsor missions. Major Duties/Responsibilities: Deploy
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including code design, documentation and testing. Familiarity with optimization methods including Machine Learning (ML) techniques. Any experience with computations on GPUs. Working knowledge of Linux command
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reasoning or tool-augmented LLMs, RL (RLHF/RLAIF/online RL), or foundation models for science, Software engineering skills (Python) and experience with modern DL stacks (PyTorch) and multi-GPU training
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container technologies in HPC environments. Experience with multiple system deployment mechanisms (Warewulf, PXEboot, Cobbler, Bright). Experience with GPU clusters (NVIDIA, AMD) for AI/ML and scientific
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utilizing GPU (NVIDIA and AMD) clusters for AI/ML and/or image processing. Knowledge of networking fundamentals including TCP/IP, traffic analysis, common protocols, and network diagnostics. Experience with