44 staticstical-quality-control Postdoctoral positions at Oak Ridge National Laboratory
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Requisition Id 17142 Overview: We are seeking a Postdoctoral Research Associate who will focus on urban-scale energy modeling. This position resides in the Grid Interactive Control Group in
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Requisition Id 17135 Overview: We are seeking a Postdoctoral Research Associate – Modeling, Controls, and AI for Thermal Systems to advance the design, operation, and maintenance of thermal systems
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environment, safety, health, and quality program requirements Maintain a strong dedication to the implementation and perpetuation of values and ethics. Occasional travel to support domestic and international
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harsh environment applications. Develop data acquisition and control systems that include hydraulic or pneumatic actuation and pressure monitoring combined with interrogation of optical fiber-based
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of projects related to electrochemical analysis, surface specific spectroscopy, and battery materials for vehicle and aircrafts. Applicants are expected to perform experiments to direct and control
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team planning, documentation, verification and validation, and manufacturing-material-related simulation quality processes Author peer reviewed papers, technical papers, reports and proposals
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. In this role, you will leverage large-scale, heterogeneous datasets to develop and deploy AI-driven methods for: Real-time quality monitoring and control of manufacturing processes Understanding
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export control requirements. Successful candidates must be qualified for such access without an export control license. For employment at Oak Ridge National Laboratory (ORNL), a Real ID compliant form
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to bring strong expertise in scalable deep learning, high-performance computing (HPC), scalable data management, Linux environments, and production-quality scripting for HPC workflows. Major Duties
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, including version control, testing, continuous integration, documentation, and code review. Knowledge of nuclear nonproliferation, international safeguards, arms control, nuclear fuel cycle analysis