77 data-mining-post-doc-"https:" Postdoctoral positions at Oak Ridge National Laboratory
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through data-driven modeling and optimization. The successful candidate will work at the intersection of thermal-fluid sciences, control theory, and artificial intelligence/machine learning to advance
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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
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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
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universities to address project objectives. Present research results within the project and at national and international conferences, and publish findings in peer-reviewed journals, and datasets in DOE data
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science and analytics at scale to enable scientific discovery across the physical sciences, engineered systems, and biomedicine and health. It provides foundations and advances in quantum information
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evolution, phase stress, and defect evolution using advanced data analysis tools. Perform alloy fabrication and processing (e.g., arc melting, heat treatment) and relate processing to performance. Apply
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characterization using advanced analytical tools, such as synchrotron, neutron, and laboratory equipment. Experience in data analytics and modeling tools. Special Requirements: Applicants cannot have received
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for geothermal casing related harsh environments applications. A background in polymer chemistry research or related fields, composite material development, material science, and data analysis is preferred. Strong