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including tensile, fatigue, creep, impact, and nanoindentation testing. Analyze lattice strain evolution, phase stress, and defect evolution using advanced data analysis tools. Perform alloy fabrication and
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for characterization and analysis of membranes and composites including X-ray/neutron powder diffraction, electron microscopy and lithium analysis using ICP, NMR, etc. Collaborate with ORNL postdocs and staff who
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analysis skills using Python, MATLAB, or similar platforms. Excellent written and oral communication skills. Motivated self-starter with the ability to work independently and to participate creatively in
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modeling, optimal power flow (OPF), surrogate modeling, and data-driven analysis of large-scale electric power system simulations on DOE leadership-class computing resources. The candidate is expected
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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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, mechanical testing (creep, fatigue, tensile properties), microstructural analysis, thermal/electrical properties will be desirable. The position will involve significant interaction with a large team within
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, additive manufacturing, solid-state processing (rolling, extrusion, wiredrawing etc.), mechanical properties (creep, fatigue, tensile properties), microstructural analysis, thermal/electrical properties will
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
. Major Duties/Responsibilities: Develop and validate AI/ML models that can be used for knowledge extraction (e.g. discovery of governing equations; correlative analysis across length/time-scales etc.) from
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storage and analysis solutions (e.g., key-value stores, object or document storage, graph analytics systems) deployed on HPC computational and storage systems. Co-authorship of peer-reviewed publications