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and wave-equation–based modeling, including familiarity with adjoint-state methods, gradient-based optimization, and multi-scale inversion strategies. Proven expertise in machine learning and deep
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at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model reduction, with
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of the entire reprogramming process, from the sourcing of somatic cells to the final characterization and banking of high-quality, pluripotent lines. This expertise involves mastering numerous
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an opportunity to contribute to a collaborative international research environment. GIANTS is a five-year theoretical and numerical modelling project focused on the late stage of terrestrial planet formation
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machine learning methods for computational materials physics and chemistry. Projects include: The aim is to develop generalized equivariant neural network models NequIP and Allegro for machine learned
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relativity, The analytical and numerical study of the potential breakdown of effective field theory methods outside black holes, The analytical and numerical modeling of the dynamics of (and gravitational
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of the academic qualifications and/or diplomas, if applicable. Enrolment in Master degree in Physics and Astrophysics. Work plan: To develop numerical methods to study the dynamics of many-body soft and living
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Context Statement Position Overview A postdoctoral position is available in the Department of Physics at the University of Idaho. The primary focus of this position is advancing numerical relativity methods
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coastal dynamics, with a focus on how human interaction shapes flow regimes, sediment processes, water quality, and environmental change. Using advanced field methods, remote sensing, and hydro and
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). Familiarity with asymptotic and multiscale mathematical analysis methods to ground proof numerical simulations Strong communication skills and ability to work across disciplinary boundaries. Desirable