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expertise in areas such as approximate inference, Bayesian statistics, continuous optimization, information geometry, etc. We work on a variety of learning problems, especially those involving supervised
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research utilizing information networks. We focus on maximizing researcher's performances by ensuring efficient network utilization and system/application optimization. Leveraging insights from our
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science. [Work content and job description] Analytical and numerical theoretical studies of condensed matter physics, with an emphasis on topological phases, spintronics, and quantum geometry–related physical
Searches related to numerical optimization
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