351 computer-security "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" "UCL" "UCL" positions at Oak Ridge National Laboratory
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methods towards improving our understanding of unique target materials. You will be working with scientists, engineers, technicians, and safety and quality assurance staff to support material testing and
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Requisition Id 16042 Overview: The Safety and Operations Services Division (SOSD) within the Environmental, Safety, Health & Quality (ESH&Q) Directorate at Oak Ridge National Laboratory (ORNL) seeks
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for the MSA Group and across the MDF Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal
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, integration, verification & validation, sustainment). This position resides in the Support Services & Isolated Environments group in the Research Computing division of the Information Technology Services
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Program requirements to maintain accountability and integrity of ORNL locking systems as well as compliance with applicable DOE Orders. This position resides in the Physical Security Team in the Security
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highly qualified individuals to play a key role in improving the security, performance, and reliability of the NCCS computing infrastructure which supports multiple highly ranked Top500 Supercomputers
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) in the National Security Sciences Directorate (NSSD). The Nuclear Nonproliferation Division conducts world-class research, technology development, risk assessment, and systems analysis in support of
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implementing operations plans within budget and schedule. Follows environmental, safety, health, and quality program requirements, including ISMS and SBMS. Deliver ORNL’s mission by aligning behaviors
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to ensure compatibility and safety of system. Inspect electrical systems, equipment, or components to identify hazards, defects, or the need for adjustment or repair and to ensure compliance with codes
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engineering. This position seeks a candidate with demonstrated experience in applied machine learning and computational data science for complex, safety-critical systems, with an emphasis on time-series anomaly