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and research in several areas. These include, but are not limited to: Adversarial location and network interdiction models Adversarial machine learning attacks and defense (e.g., against Bayesian
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 7 hours ago
and Bayesian methods. Experience using DOE computing allocations (NERSC, OLCF, ALCF). A record of productive collaboration in multi-institutional projects. Special Physical/Mental Requirements Special
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recognized research centers and institutes. The incumbent will join a collaborative department with internationally recognized expertise in biostatistics, statistical omics, bioinformatics, Bayesian
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, characterization, optimization, and autonomous decision-making. Advance Bayesian optimization, active learning, machine learning, scientific models, genetic algorithms, and AI agents in physical laboratory systems
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to contribute to developing new ones Fluency and experience with high performance and/or high throughput computing Expertise in the theory and usage of high-level Bayesian and/or frequentist statistical data
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for mathematics across the CWTS Leiden, ARWU, USNews, and QS rankings. In Statistics, the School has research strengths in Bayesian and Monte Carlo Methods, Biostatistics and Ecology, Combinatorics, Data Science
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for mathematics across the CWTS Leiden, ARWU, USNews, and QS rankings. In Statistics, the School has research strengths in Bayesian and Monte Carlo Methods, Biostatistics and Ecology, Combinatorics, Data Science
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(including Bayesian, frequentist, data mining, artificial intelligence, and machine learning), and design, develop, and validate predictive statistical models, specifically for student enrollment management
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, including active learning or Bayesian optimization. Experience with imaging, time-series or high-dimensional data. Exposure to crystallography or structural biology. Experience with multimodal datasets and
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the same systematics identified in the observables. d) Estimation of cosmological and “nuisance” parameters using Bayesian methods. 4. The research activities provided for the post-doc assignment will