30 data-mining Postdoctoral positions at Oak Ridge National Laboratory in United States
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on ab initio data to accurately model the thermodynamic and thermophysical properties of complex materials. Conduct molecular simulations to elucidate the thermodynamic and structural basis of enhanced
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visual representation and analysis of large-scale 2D/3D scientific data. This position resides in the Data Visualization Group in the Data and AI Systems Section, Computer Science and Mathematics Division
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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
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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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maturation, characterizing performance and properties of nuclear fuels and materials, and generate the data to advance physical modeling and simulation. The primary function of this open position is to perform
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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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magnetism or superconductivity. Experience operating and maintaining Quantum Design instruments. Proficiency with x-ray diffraction measurements and data analysis. Familiarity with the health and safety
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spectroscopies, or nanophotonics Basic understanding of data analytics techniques. Preferred Qualifications: Experience with spin-based or squeezed quantum sensors. Experience with dilution refrigeration
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characterization techniques with mechanical behavior and finite element methods. The postdoctoral candidate will develop the processes needed to connect mechanical testing data with 3D microstructure of nuclear
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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