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fields: Agentic AI, Large Language Models, Artificial Intelligence, Biomedical Ontologies, Biomedical Knowledge Graphs, Computational Biology, Bioinformatics, Biomedical Informatics or a related field
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application to quantum theory and information science. Other application areas of interest include robust parameter estimation and performance bounds under model misspecification, integrated sensing and
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Qualifications at this Level Education/Training: PhD (theoretical nuclear/high-energy physics, quantum information science, lattice gauge theories, quantum many-body dynamics) Experience: Preferred--computational
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interpretable patterns from large, messy, real-world datasets (e.g., panel data, clickstream data, intervention data, survey and psychometric data) • Contributing to theory development in behavioral economics and
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operates at the intersection of epidemiologic theory, quantitative methods, and translational science, with a strong emphasis on rigorous study design, reproducible analytics, and integration of biological