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Field
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in one of the following areas: Machine Learning / Information Retrieval / Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date machine learning
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magic-angle spinning experiments, multinuclear NMR, quadrupolar nuclei, or two-dimensional NMR methods, is highly desirable. Experience with computational methods such as density functional theory
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influence collective response, localization, failure, and critical behavior. Design and interpret numerical studies and, where appropriate, help connect theory and computation with experiments, data
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implementation science theories and methods. Excellent interpersonal, verbal, written communication, and organizational skills. Experience with institutional review boards (IRB). Preferred Qualifications Prior
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-driven dynamical systems, perturbative theories in celestial mechanics, symbolic regression, inverse methodologies via differentiable simulations, efficient high-dimensional numerical quadrature and more
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foundations to industrial and scientific applications. The project will be conducted at the Machine Learning and Decision Making Lab , which focuses on advancing the theory and practice of machine learning and
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Theory, Department of Environmental and Energy Sciences. Become a part of the team and contributing to research on current and future mobility service usage and attitudes among car and non-car owners
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closely with prof. Roel Leus on advancing the state of the art in decision diagram-based methods for combinatorial optimization, including the development of new theory, algorithms, and high-impact
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Position Description The Quantum Condensed Matter Theory group at BU anticipates awarding a postdoctoral associate position under the supervision of Professor Dries Sels. The position will broadly
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-interaction treatment of SOC, while scalar relativistic effects will be included via the spin-free exact two-component theory in its one-electron variant (SFX2C-1e), thereby extending the scope of EOM-CC