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Field
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Multiple Research-Intensive Associate/Full Professor Tenure System Positions & an 1855 Professorship
, implementation science, geospatial analysis, biostatistics and research design, analysis of interventions (e.g., difference-in-differences), AI analytics, agent-based modeling, Bayesian modeling, causal inference
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of Bayesian inference, data science, and machine learning. Experience in scientific computing in C++, Python, and/or Julia. Knowledge of electrochemistry and materials science (desirable). Ability to work as a
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multivariate statistics, dimensionality reduction, and latent variable modeling. Experience with temporal or dynamical modeling, Bayesian inference, and survival analysis for clinical outcome data. Scholarly
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of multi-modal healthcare record data. The ideal candidate will additionally have experience: Multi-modal AI model development Statistical modelling techniques (Bayesian inference, differential equations and
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interdisciplinary collaboration. Faculty and students conduct innovative research in Bayesian methods, causal inference, data science, machine learning, statistical genetics, longitudinal and survival analysis, and
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, the emulator will enable Bayesian analyses of relativistic nuclear functionals that are presently out of reach. With the emulator in place, Bayesian inference will be used to constrain nuclear model parameters
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on Bayesian small area estimation (SAE) methods that borrow statistical strength across space and time when local data are insufficient. Building on the Fay-Herriot model and its spatiotemporal extensions
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Observatory. This person will work under Chad Hanna, and their responsibilities include leading projects in real-time gravitational wave detection and parameter inference of neutron stars and black holes
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modeling, sensitivity and robustness analysis, Bayesian inference, inverse problems, parameter estimation, or model validation. Experience or strong interest in scientific AI/ML, including surrogate or multi
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Bayesian deep learning (e.g., Monte Carlo dropout, deep ensembles, Laplace approximations, and variational inference), several challenges remain: Scalability: Many Bayesian inference methods