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on developing, implementing, and applying tensor-network and related quantum-dynamics methods. Applicants may indicate a preference for one project or ask to be considered for both. The start date is flexible
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funded NIH R01 project dissecting the SOX4/SOX9 transcriptional regulatory network that governs biliary development in Alagille syndrome and advancing the therapeutic potential of targeting this network in
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pavement networks. This position will focus on modeling and optimizing pavement performance under evolving climate and hazard conditions. This position will advance modeling of pavement life-cycle
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: Ph.D. in Physics, Computer Science or closely related areas Preferred: Demonstrated strength in one or more of the following. - Quantum many-body numerics: tensor-network methods, variational Monte Carlo
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Semiconductor Manufacturing and Research Technology Innovation Corridor (NY SMART I-Corridor) Tech Hub initiative, the Center for Supply Chain Analytics leads the Supply Chain Activation Network (SCAN) project
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University of North Carolina at Greensboro | Greensboro, Indiana | United States | about 3 hours ago
institution in North Carolina for net cost by the N.Y. Times and No. 1 in North Carolina for social mobility by The Wall Street Journal — helping first-generation and lower-income students find paths
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educational opportunities, networking and presenting at local, national, and international meetings, and reading current primary literature. Provides a friendly working environment and be available to teach
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based at Yale School of Medicine for 25 years. It is an international, collaborative network of epidemiologists, statisticians, pharmacoepidemiologists, and clinicians with an exceptional record
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. Opportunities to Contribute Develops dynamic river‑lake network models to assess geochemical and ecological changes—including carbon cycling and elemental fluxes—resulting from enhanced rock weathering. Writes
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fundamental research in physics-informed and symmetry-aware machine learning for nonadiabatic excited-state molecular dynamics. Develop and evaluate equivariant graph neural networks and related architectures