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that gap by exploring connections between AI models and low-rank tensor decompositions, providing a rigorous mathematical framework to address key questions:- When are learned representations interpretable
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for new quantum computing algorithms. It will rely on statistical structure learning represented by knowledge graphs and efficient low-rank tensor compressions. We are looking for: A completed scientific
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quantum theory and mathematics. Ideally, you should have experience with the usual tools of quantum information (e.g. tensor network calculus, quantum communication protocols, quantum tomography protocols
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information (e.g. tensor network calculus, quantum communication protocols, quantum tomography protocols), linear algebra, and programming (e.g. Mathematica/ Python/ Matlab). You have an excellent command
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, for example via equilibrium and non-equilibrium Green's functions, density functional theory, and tensor network methods. Experience in topological magnetic systems and nonlinear optical phenomena is considered
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efficiently work in a highly multidisciplinary team. You have a strong background in linear and tensor algebra, non-linear mechanics, advanced finite element analysis, computational solid mechanics
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involved in analyzing data collected from the TexNet seismological monitoring program and other stations or assets that provide quality data. Comparing different methods and tools for moment tensor inversion