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infrastructure, opportunities to collaborate with faculty at a truly interdisciplinary school, and connections to the policy world and to a robust network of diverse scholars. This is an exempt-level, benefited
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and membrane protein complexes • Familiarity with Linux, MATLAB, Python, or other computational tools is a plus This position provides an excellent opportunity to work on high-resolution structural
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network modeling to address real-world policy challenges through algorithm development and technical analysis. Key Responsibilities Conduct original research in generative AI Train and supervise graduate
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)—topics including, but not limited to: · Physics-informed neural networks (PINN) & neural operators · Physics-aware convolutional neural networks (PARC) · Meta-learning/transfer
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. This position offers a unique opportunity to contribute to high-impact, interdisciplinary research at the intersection of network science, global research competitiveness, and generative AI capabilities
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science of science, network science, and natural language processing. As part of a small research team, the postdoc will help lead efforts to provide a quantitative model of global competitiveness
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research using complex observational healthcare data, with a focus on cancer studies. The successful candidate will be expected to: Modeling multilevel survival data while addressing confounding and missing