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) section in the Chemical Sciences Division (CSD), Physical Sciences Directorate (PSD) at Oak Ridge National Laboratory (ORNL). As part of our research team, you will support advanced radiochemical operations
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(Power, Space, Cooling) metrics integration. You’ll design, implement, and continuously improve the physical and logical infrastructure across our enterprise facilities, and build data-driven visibility
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for services and evaluation of customer satisfaction. Strong verbal communication skills Special Requirement: Work involves various physical requirements and working conditions. As such, a pre-hire physical will
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for combining/separating chemicals, compounds, and related products and materials using principles and technology of chemistry, physics, mathematics, engineering and related sciences Assist IPMD supervision in
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, condensed-matter physics, neutron scattering, applied mathematics, and scientific computing. This position resides within the Quantum Sensing and Computing Group in the Computational Sciences and Engineering
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key performance metrics that demonstrate progress, effectiveness, and impact across the division. Lead process improvement initiatives that streamline operations, enhance productivity, and strengthen
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-specific vacuum and process gas compatibility guides. Work with Manufacturing and Quality staff (both internal to ORNL and with external suppliers) to incorporate non-conformances and manufacturing feedback
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activities. Specify materials conforming to national standards (e.g., ASTM, MIL, SAE, UNS, etc.) based on required material properties and technology-specific vacuum and process gas compatibility guides
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implementation of constitutive models within commercial and/or open-source finite element software is required. Preferred Qualifications: Demonstrated expertise in multi-physics FE simulations is preferred
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disturbance at scale. A cohort of postdoctoral researchers will be hired across multiple institutions to collaboratively support scientific advances guided by AI/ML, remote sensing, process-based modeling