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-destructive testing, machine or process monitoring, or similar applications of algorithmic tools. Demonstrated expertise in the adoption of modern machine learning tools for time series analysis Preferred
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pertains to nuclear fuel cycle. Demonstrated experience in Python, MATLAB, R, or similar. Demonstrated experience with applying techniques to introduce uncertainty quantification to machine learning
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of the field through the development and use of machine learning, deep learning, and high-performance computing (HPC). This position resides in the Chemical Separations Group in the Separations and Polymer
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concerns, using a questioning attitude, considering hazards for every task, and never stop learning. Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact
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significant problems, and you will apply your work to exciting research in multi-disciplinary domains alongside globally recognized experts. You will bring creative thinking, teamwork, and machine learning
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the organization. Establish an environment where learning never stops, honest mistakes are treated as opportunities to learn, and challenges are welcomed to improve performance. Encourage open and ongoing dialogue
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objectives Develop secure area assessment schedules and facility certification plans Conduct annual secure area assessments, auditing, and testing, incorporating lessons learned into plan updates Ensure
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materials such as the magnetoelectric, high entropy oxides, through neutron scattering experiments. Additionally, collaborative work will be performed with the aim of developing and applying machine learning
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areas including: Quantum information sciences, Artificial intelligence and machine learning, Biotechnology, High-performance computing, Semiconductors, and Advanced materials and manufacturing. Generate
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in the Manufacturing Demonstration Facility (MDF - https://www.ornl.gov/facility/mdf/ ) where it focuses on designing, developing, and deploying advanced machine learning and decision science