43 combustion-modelling-postdoc Postdoctoral research jobs at Oak Ridge National Laboratory
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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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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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for radiation protection and develops many of the biokinetic and dosimetric models recommended by the International Commission on Radiological Protection (ICRP) and applied by U.S. federal agencies
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national security, proliferation detection, and nuclear forensics applications. This position resides in the Collection Science and Engineering Group in the Material Characterization and Modeling Section
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include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced
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research analysis on geothermal well development and other advanced energy technologies that could achieve transformative gains in energy efficiency. Ability to develop optimization and life cycle models
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thermomechanics. Major Duties/Responsibilities: Help to develop and apply physics-based and/or machine learning models for advanced manufacturing processes. Author peer reviewed papers for journals and conference
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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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characterization, and predictive fault tolerance in HPC systems. Architectural exploration and performance modeling of high-bandwidth memory (HBM) and DDR memory systems in the context of data-intensive scientific
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(e.g., deep learning, implicit neural representations, diffusion models) for CT reconstruction, enhancement, and defect detection. Advance algorithms for multi-modal tomography (X-ray, neutron, electron