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members have been working on statistics learning, granular computing and knowledge discovery, machine learning, deep learning, and specifically interpretable artificial intelligence. Many innovative
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-- Deep learning for nuclear physics -- Effective field theory for nuclear structure -- Hard Probes of Quark-Gluon Plasma -- Hot and cold lattice QCD -- Physics in electron-ion collisions -- Relativistic
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Research, or a related field. Solid research background and practical experience in one or more of the following areas: Reinforcement Learning / Deep Reinforcement Learning Fine-tuning and Application
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, machine learning, deep learning, and specifically interpretable artificial intelligence. Many innovative contributions have been achieved in theory, methodology and applications, including high-level papers