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of correlated and topological materials. Work in thin film synthesis and physical property characterizations. Experience in user facilities. Complete data analysis of structural, electronic, magnetic, and
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recovery. Perform and/or direct characterization of products of these dry head-end processing and subsequent separations, including identifying information gaps in support of flowsheet development. Implement
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well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs. Special Requirements: Postdocs: Applicants cannot have received
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the laboratory. Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs. Special Requirements: Postdocs
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Requirements: Postdocs: Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment
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training algorithms and AI architecture Image reconstruction, segmentation, and classification High performance computing for spatiotemporal data Major Duties/Responsibilities: Develop foundation AI models
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, transportation, and more, with a special emphasis on grid resilience assessments and equity analysis. You will have the opportunity to creatively use interdisciplinary methods from computational data science
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objectives. Basic Qualifications: A Ph.D. in materials science, physics, chemistry, nuclear engineering, or related disciplines. Preferred Qualifications: Experience with ellipsometry Experience with data
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evolutionary/quantitative/population genetic analyses on existing large datasets. Apply and develop bioinformatic pipelines to analyze diversity data, especially in the context of pan-genome resources. Work
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NCCL. Knowledge of high-performance computing and its applications. Additional Information: Applicants cannot have received their Ph.D. more than five years prior to the date of application and must