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developing software in a Linux environment. Experience running numerical experiments on shared HPC facilities (e.g., GPU clusters). Experience in team collaboration in software development and awareness
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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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) general-purpose hardware such as accelerators for AI and ML, high-performance computing, low-power edge computing, quantum computing, cybersecurity, chiplets, and CPU, TPU, GPU, and FPGA systems; or (2
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mathematics, machine learning, uncertainty modeling and/or statistics; developing in a Linux environment; high-performance, GPU, or cloud-computing experience. Additional experience in the areas mentioned in
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, GPU, or cloud-computing experience. Proven ability to work independently, formulate research questions, and take initiative. Cumulative GPA of 3.0. General Notes An agency designated by the federal
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at MGHPCC facility (i.e., data center, compute, storage, networking, and other core capabilities). Deploy, monitor, and manage CPUs, GPUs, storage, file systems, networking on HPC systems. Develop and deploy
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. Familiarity with data formats common in scientific domains such as medical imaging, genomic sequences, proteins, chemical structures, geospatial, oceanographic, and heath record data. Experience in CUDA GPU
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, VMware. Experience building and running containerized applications in an HPC environment. Knowledge of Apptainer, Warewulf, Fuzzball. Experience managing systems using GPU/CUDA clusters for AI/ML and/or
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-focused GPUs. Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a
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libraries like NCCL/RCCL and experience with high performance computing middleware is highly desirable. Optimizations of large parallel code bases and experience with GPU programming languages such as CUDA