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of numerical quantum many-body methods to study model Hamiltonians. Strong background in linear algebra. Preferred Qualifications: Experience with density matrix renormalization group and tensor network
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for kinetic and/or fluid equations Multiscale problems and model reduction Modern machine learning software tools and frameworks Implementation of scalable numerical algorithms on HPC architectures Excellent
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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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-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and uncertainty quantification. The position comes with a travel allowance and access
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, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced computing resources. The MMD group is responsible
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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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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
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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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of application development techniques (numerical methods, solution algorithms, programming models, and software) at scale (large processor/node counts). A record of productive and creative research as proven by