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diffraction (EBSD), and transmission electron microscopy (TEM) for microstructural characterization of materials. Correlate irradiation conditions to the post-irradiation performance of novel materials
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to direct and control 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
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, from process execution to post-process characterization. This environment enables the creation of high-fidelity digital twins and AI-ready datasets that support real-time monitoring, predictive
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software tools for integrated system dynamic and transient simulations. Integrate post-processing measures for simulations to help with automation. Deliver ORNL’s mission by aligning behaviors, priorities
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years A minimum of 2 years of post-Ph.D. experience utilizing computational thermodynamic (CALPHAD) software, such as Thermo-Calc, DICTRA, PANDAT, or FactSage. Proficiency in materials data analytics
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-principles electronic structure calculations (e.g. DFT and post-DFT methods) for generating datasets to train AI models leveraging DOE’s HPC platforms Develop new methodologies that can describe both
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Department of Energy (DOE) Order 473.1A, which requires a favorable post-employment background investigation. To obtain this credential, new employees must successfully complete and pass a Federal
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Department of Energy (DOE) Order 473.1A, which requires a favorable post-employment background investigation. To obtain this credential, new employees must successfully complete and pass a Federal
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, which requires a favorable post-employment background investigation. To obtain this credential, new employees must successfully complete and pass a Federal Tier 1 background check investigation
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Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, which requires a favorable post-employment background investigation. To obtain this credential, new