244 data-"https:"-"https:"-"https:"-"https:"-"Ulster-University" positions at Oak Ridge National Laboratory in United States
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/Output Controllers (IOCs), Operator Interfaces (OPIs) and networking. Maintain EPICS services, including data archiving, alarming, and gateway services; monitor performance and plan upgrades as needed
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(MiC) Section in the Computer Science and Mathematics (CSM) Division. The MiC Section creates the mathematics and algorithms that harness machines, ideas, and data, to enable far reaching scientific
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records, information systems, and process improvement. This role focuses on modern records practices, including electronic records management systems, digitization initiatives, system integrations, and
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include: Information and Communication Management: Efficiently manage information flow and communications, ensuring alignment with the Division Administrative Assistant and other administrative staff
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workflows, and data infrastructure to accelerate discovery in Populus genomics and the characterization of Populus-associated microbial communities. The successful candidate will design and implement scalable
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compliant, mission-focused, and employee-centered workplace. Major Duties/Responsibilities: Workforce Data & Reporting: Generate and analyze workforce data and standard HR reports to support HR decision
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infrastructure, ensuring smooth data communication by managing and configuring network devices like routers, switches, firewalls, and wireless access points, monitoring network performance, identifying and
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of the infrastructure. Proactively monitor, test, collect and analyze system performance statistical data to improve quality of the network environment. Diagnose and resolve complex configuration and troubleshooting
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. Support operability assessments and review equipment performance data to identify trends or vulnerabilities. Participate in system walk-downs, evaluate system health, and help identify spare-parts and
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, supported by: Multiscale modeling (material (molecular) → process → manufacturing (scale up)) Data-informed experimentation Selective use of AI/ML and big-data techniques where they add real value