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/EXPERIENCE: Numerical machine leaning/Scientific Machine Learning are highly expected Preferred research directions: Numerical Optimization and Scientific Machine Learning. A PhD in Mathematics or Applied
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, China [map ] Subject Areas: Physics / Atomic Molecular and Optical Physics , artificial gauge fields , Cold Atom Physics , Condensed Matter Theory , Machine Learning , Quantum Hydrodynamics , Quantum
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forefront of the country. Software engineering, machine learning and other directions have formed their own characteristics and advantages in the international competition, and have had an important impact at
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processing 2. nonequilibrium physics of molecular machines 3. understanding the role of phase-separated condensates with quantitative models 4. Modeling multicellular systems in mechanobiology 5. AI assisted
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Peking University, Center for Machine Learning Research (CMLR) Position ID: 2952 -FACULTY [#24742] Position Title: Position Type: Tenured/Tenure-track faculty Position Location: Beijing, Beijing
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, bio-inspired amphibious robots design as well as AI application in vortical flow control and sensing. Based on physics-informed (and -informative) machine learning, we combine domain expertise (fluid
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, bio-inspired amphibious robots design as well as AI application in vortical flow control and sensing. Based on physics-informed (and -informative) machine learning, we combine domain expertise (fluid
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courses such as digital economics, econometrics, digital industrial organization, digital finance and securities investment, statistics, machine learning and data mining, database technology and application
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open-ended About the POSITION This position focuses on developing and applying advanced computational approaches - including Artificial Intelligence, machine learning, and data science - to address
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Ion Physics Nuclear Physics / Lattice QCD Lattice Field Theory Machine Learning / Machine Learning lattice gauge theory (more...) High Energy Physics / Theoretical Particle Physics Appl Deadline