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) Familiarity with high-performance computing (HPC) and distributed training environments Hands-on benchmarking/Test & Evaluation of AI systems Interest in AI applications for safety, risk modeling, or scientific
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CPX-I pneumatic and digital IO distributed control systems Design of distributed control systems using IO-Link, Ethernet/IP, Modbus, and other industrial network protocols Design of industrial control
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distributed codes using MPI, OpenMP, CUDA, ROCm, and related HPC technologies while bridging theoretical AI models with real hardware constraints. Cross‑Paradigm Integration(new optional emphasis): Explore how
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working with spatio-temporal datasets and remote sensing imagery Knowledge of distributed computing and uncertainty quantification Ability to function well in a fast-paced research environment, set
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implementation for core capabilities such as distributed training and inference, workflow orchestration, GPU/accelerator utilization, model registries and artifact management, vector search and retrieval-augmented
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Reporting: Compile and distribute monthly performance reports. Team Collaboration: Provide backup support to the division office and collaborate with other administrative staff as needed. Special Projects
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of relevant experience Experience with mechanical testing and ceramic powder characterization (e.g., particle size distribution, specific surface area, pycnometry and x-ray diffraction, and tap density
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-based or LiDAR-inertial SLAM, high-accuracy point cloud mapping, or scan-to-scan registration. Experience with ROS 2 navigation stack, lifecycle-managed nodes, TF2, rosbag workflows, and distributed
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for Science @ Scale: Pretraining, instruction tuning, continued pretraining, Mixture-of-Experts; distributed training/inference (FSDP, DeepSpeed, Megatron-LM, tensor/sequence parallelism); scalable evaluation
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. Demonstrated programming ability and knowledge of Python and/or C++. Experience with deep learning frameworks like PyTorch and application on high-performance computing (HPC) environments using distributed data