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for process industries (e.g., chemical, pharmaceutical, materials, energy) or discrete manufacturing (e.g., electronics assembly, automotive, home appliances). Explore the integration of large language models
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are especially encouraged: (1) statistical mechanics, condensed-matter theory, quantum field theory; (2) tensor networks, quantum information, quantum algorithms; (3) machine learning, generative models, large
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modeling Qualifications Applicants should have a Ph.D. in mathematics, applied mathematics, computer science, engineering, computational science, physics, or a closely related field by the start date
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generating functions, and discovered algebraic P-fields, initiating an algebraic-geometric construction of A-model Landau–Ginzburg string theory. His recent interests include the mathematical formulation
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Develop industrial process digital twin models based on the fusion of mechanistic and data-driven approaches. Develop predictive maintenance and fault diagnosis algorithms for critical equipment (e.g
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appliances). Explore the integration of large language models and reinforcement learning for real-time optimization, fault self-recovery, and production scheduling in industrial processes. Publish research
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) or discrete manufacturing (e.g., electronics assembly, automotive, home appliances). Explore the integration of large language models and reinforcement learning for real-time optimization, fault self