321 application-forms-"https:"-"https:"-"https:"-"Stanford-University" positions at Oak Ridge National Laboratory
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Qualifications: Requires a bachelor’s degree in computer science, computer engineering, or related field and a fundamental understanding of cyber security principles, tools, and operational applications. Preferred
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applications. We seek individuals with technical and analytical skills who are comfortable working across disciplines to contribute and lead in fast paced, team-oriented environment. Successful candidates will
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Eligibility Requirements: For employment at Oak Ridge National Laboratory (ORNL), a Real ID compliant form of identification will be required. Additionally, ORNL is subject to Department of Energy (DOE) access
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across DOE laboratories, universities, and partner agencies to broaden the applications of AI-enabled hydrological modeling. Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with
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the candidate will manage are leading competitions for large first-of-a-kind equipment purchases, professional and consulting services, etc. This position resides in the Contracts Division in the Business
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, and maintain computational workflows that enable reproducible, scalable science on leadership-class systems. You will collaborate with researchers across diverse domains to translate scientific
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adapt to ever changing needs. Special Requirements: For employment at Oak Ridge National Laboratory (ORNL), a Real ID compliant form of identification will be required. Additionally, ORNL is subject to
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advancements with a variety of applications, from national security to life-saving medical treatments. ESED is the national steward for the research, development, and demonstration of centrifuge technology
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manufacturing for applications in aerospace, automotive, and marine/shipbuilding industries, as well as trending areas of international composite research in material science, mechanical engineering, chemical
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postdoctoral research associate to advance the state of scientific AI by addressing cross-cutting challenges in data readiness for AI to enable scalable, reproducible AI workflows on leadership-class systems