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manufacturing datasets, including sensor streams, in-process signals, post-process characterization data, simulation outputs, and digital twin data. Develop, integrate, and evaluate AI/ML models for anomaly
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Requisition Id 16485 Overview: The Data and AI Systems Research Section within the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral
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data science to develop new methodologies for assessing and improving the quality of components fabricated using advanced manufacturing processes. This position resides in the Manufacturing Systems
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magnets, batteries, and semiconductors from mined resources and electronic waste, as well as separations for bioenergy applications. Develop new research directions and contribute to proposals for external
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. The position offers the opportunity to work at the interface of model development, observational data synthesis, and emerging AI/ML methods, in close collaboration with researchers from the SPRUCE (Spruce and
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. The position offers the opportunity to work at the interface of model development, observational data synthesis, and emerging AI/ML methods, in close collaboration with researchers from the SPRUCE (Spruce and
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interfacial reactions within energy storage materials. In addition, the applicant will be expected to help train new scientists (graduate students, post-BS, and post-docs) with suitable laboratory procedures
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to design and implement experiments, perform data analyses, and interpret experimental results. Excellent interpersonal, oral, and written communication skills. Preferred Qualifications: Demonstrated
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analysis and technoeconomic analysis without significant direction from ORNL staff Leverage primary equipment data from both within ORNL and from industrial partners from across the U.S. to quantify costs
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time-of-flight secondary ion mass spectrometry (ToF-SIMS), scanning electron microscopy (SEM), and X-ray diffraction (XRD). Experience in data reduction of big spectroscopy, mass spectrometry, and image