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resources and demonstrated ability in applying numerical techniques to water-energy research. Strong candidates will have advanced knowledge and skills relevant to one or more of the following areas: River
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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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experience in hydrological or Earth system modeling, with emphasis on process understanding and prediction. Strong background in computational sciences, including numerical methods, high-performance computing
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five years. Demonstrated expertise in computational mechanics and numerical modeling Experience in polymer composite manufacturing processes Experience with simulation tools for thermomechanical analysis
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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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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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a particular emphasis on error-corrected methods for future fault-tolerant quantum computing. The algorithms will be designed to address key models of quantum materials, such as the Hubbard model
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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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development techniques (numerical methods, solution algorithms, programming models, and software) at scale (large processor/node counts). Experience with use of artificial intelligence and machine learning in
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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and