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(Xilinx Vitis/Vivado, Intel Quartus, HLS tools) HPC environments or GPU-accelerated computing On-detector firmware or data acquisition systems Familiarity with HEP data formats and reconstruction
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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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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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. Demonstrated experience developing and running computational tools for high-performance computing environment, including distributed parallelism for GPUs. Demonstrated experience in common scientific programming
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or OpenMP. Experience in heterogeneous programming (i.e., GPU programming) and/or developing, debugging, and profiling massively parallel codes. Experience with using high performance computing (HPC
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(GPUs). Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory. Ability to function well in a fast-paced research
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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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software engineering practices. Experience with GPU computing (e.g., CUDA, HIP), parallel computing (e.g., MPI, Actor Model). Familiarity with containerization (e.g., Docker, Podman, Apptainer), networking