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Science and Artificial Intelligence. The lab models, simulates, and analyzes social phenomena using computational, experimental, and theoretical methods. We pursue consequential questions
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prior research and specific work, methods, datasets, systems, or research questions pursued by our lab The applicant’s personal intellectual and technical contribution to the submitted publications
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interdisciplinary-science venues. The successful candidate will be expected to formulate research questions, lead empirical projects, develop rigorous computational or experimental methods, publish in highly
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extensive experience in the application of molecular dynamics simulations to molecular systems, ideally in both methods and applications. Experience in biomolecular simulations particularly nucleic acid
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or quantitative methods. We are open to a range of methodological backgrounds, including digital trace data analysis, natural language processing, machine learning, experimental design, causal inference, and
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computational methods for the control, optimization, and coordination of complex dynamical systems. The work will emphasize applications to multi-agent systems, including robotics, autonomous systems, and
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characterization, and computational modeling. Prior experience with solid-state NMR spectroscopy, including magic-angle spinning experiments, multinuclear NMR, quadrupolar nuclei, or two-dimensional NMR methods, is
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-relationships, materials optimization, materials under extreme conditions, and generative AI. Candidates must possess substantial experience in artificial intelligence and machine learning methods, specifically
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magic-angle spinning experiments, multinuclear NMR, quadrupolar nuclei, or two-dimensional NMR methods, is highly desirable. Experience with computational methods such as density functional theory
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods