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., ML Ops for geospatial data, real-time analytics, cloud/HPC hybrid, edge geospatial compute, neuromorphic, quantum), pilot new capabilities, foster innovation partnerships (academia, industry, federal
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for structured and unstructured data Familiarity with high-performance computing, cloud environments, or distributed data systems Familiarity with uncertainty quantification methods in AI/ML Ability to present
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candidate will bring a strong foundation in systems architecture, a working knowledge of cluster computing and scaling, and a passion for advancing the security of AI systems under real-world and simulated
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at scientific edge systems using large-scale HPC/AI computational and storage systems. Design and evaluation of ephemeral, user-configurable, and composable data and storage systems. Evaluation of cloud data