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, and parallel computing, with a proven ability to work within highly secure and regulated environments. This role involves close collaboration with security teams, scientists, and IT leadership to ensure
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orchestration (e.g., Airflow, Prefect, Dagster, Make/Snakemake) and working in Linux/HPC environments. Experience with large, multi-resolution geospatial datasets and performance-oriented processing (tiling
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strategic management and strict adherence to security protocols. We are looking for candidates with extensive experience in either classified HPC data center operations, architecture, parallel computing
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and machine-learning-driven optimization frameworks for polymer composite manufacturing processes. This position resides in the Composites Innovation Group in the Manufacturing Science Division (MSD
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and clustered computing services to researchers who process large data sets and/or develop code as a part of their project. Ensure the availability, performance, scalability, and security of production
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environments. Leverage cloud object storage (e.g., Amazon S3) for data staging and artifacts; implement parallel, secure data movement and lifecycle policies. Basic Qualifications: Ph.D. in Computer Science
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on designing system software for automating processes such as intelligent data ingestion, preservation of data/metadata relationships, and distributed optimization of machine learning workflows. Collaborating
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systems, high-speed parallel file systems, and archival solutions critical to advancing scientific discovery and innovation. As part of ORNL’s leadership-class computing ecosystem, you will play a vital
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-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources
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journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation