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column chromatography, recrystallization, and preparative HPLC. Strong expertise in chemical characterization and analytical methods, including NMR, UV-vis, FT-IR, mass spectrometry, HPLC, and related
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related to magnetic materials, experience with first-principles electronic structure methods and proven expertise in developing and/or applying advanced AI/ML methods for accelerated materials discovery
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or related Applied Economics discipline with a strong quantitative focus. Demonstrable knowledge of applied econometrics and statistical evaluation methods, such as: Difference-in-Differences (DiD
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candidate will join the Multiscale Materials (MsM) group within the Advanced Computing Methods for Physical Sciences Section in CSED. The MsM group is dedicated to delivering multiscale, multi-fidelity
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will develop materials and processing technologies that will result in a transformational improvement in gallium and Germanium and rare earth magnetic material extraction methods from naturally occurring
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HPC research within past five years. Preferred Qualifications: The ability to work independently and develop and deploy methods at scale. Experience in high-performance computing and software
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. In this role, you will leverage large-scale, heterogeneous datasets to develop and deploy AI-driven methods for: Real-time quality monitoring and control of manufacturing processes Understanding
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Requisition Id 16562 Overview: The Analytics and AI Methods at Scale (AAIMS) group at the National Center of Computational Science (NCCS) at the Oak Ridge National Laboratory (ORNL) is seeking
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-loop” optimization environments, enabling AI agents to analyze, transform, and validate IR with performance-driven reasoning. HPC System Co‑Design: Investigate methods through which AI agents guide
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research within past five years. Preferred Qualifications: Ability to work independently to design and deploy methods at scale. Familiarity with hardware-software co-design, memory hierarchies (DDR, HBM