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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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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
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centered on Microsoft Azure and related AI cloud technologies with High Performance Computing (HPC) for on premise AI environments and Azure, AWS, and GCP for off premise environments. Major Duties
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strategic goals into executable roadmaps and annual operating plans. Drive delivery of high-priority initiatives (e.g., secure infrastructure, AI adoption, cloud strategy) by coordinating across divisions
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attracting scientists from all over the world conducting world-class research in physical, chemical, and materials sciences. More details about the instruments and their scientific impact can be found at https
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, Cloud, and HPC continuum. This position centers on advancing intelligent workflows that enable seamless processes in autonomous discovery, complex data integration, workflow provenance, and interactive
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such as Service Now and Atlassian products Experience with Azure or other cloud technologies Experience supporting DOE facilities or other government entities Familiarity with large-scale research
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for the design and analysis of computational methods that accelerate data analytics and machine learning, especially as the apply to scalable high-performance computing, cloud computing, and large interconnected
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all group projects, ensuring alignment with emerging industry and research trends. Drive the adoption of modern data technologies such as cloud-native architectures, distributed computing frameworks
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authorization standards, including SAML, OAuth 2.0, OpenID Connect (OIDC), and LDAP. Familiarity with managing user identities and group memberships in LDAP and Active Directory Understanding of cloud-native IAM