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Methods and Dynamics (MMD) Group at Oak Ridge National Laboratory (ORNL) is seeking several qualified applicants for postdoctoral positions related to Computational Methods for Data Reduction. Topics
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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imaging experiments at the MARS beamline (HFIR CG‑1D), including experiment planning, operando support, data reduction, and delivery of quantitative reconstructed datasets Develop and implement scattering
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: Experience in one more of the following areas: Mathematical methods for kinetic and/or fluid equations Multiscale problems and model reduction Modern machine learning software tools and frameworks
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. The Learning Systems Group at the Oak Ridge National Laboratory focuses on artificial intelligence and computational research and applies this knowledge to support the nation’s leading initiatives. We hire top
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Qualifications: Extensive experience with twisted materials fabrication and characterization. Knowledge of correlated-electron physics. Experience with spin-polarized scanning tunneling microscopy and quantum
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performance modeling, static analysis, or PIM/heterogeneous architecture research. Knowledge of large-scale scientific computing applications and algorithms (sparse linear system solvers, dense matrix
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, and access world-leading research computing facilities—all while working on problems of genuine national significance. We seek outstanding candidates with broad knowledge of hydrology and water
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the last 5 years Preferred Qualifications: Strong background in experimental systems related to heat and mass transfer systems. Knowledge of CFD tools and analytical modelling is preferred. Experience with
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physics-informed and physics-ML hybrid approaches that integrate domain knowledge with data-driven methods to advance hydrological process understanding and prediction. Conduct multimodal, multiscale data