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, active learning, Bayesian optimization, agentic AI, or closed-loop materials discovery. Experience in computational heterogeneous catalysis, electrocatalysis, surface science, electronic-structure analysis
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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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the production of the CBC catalog. The successful candidate is expected to have strong analytical skills and experience with signal processing, Bayesian statistics and machine learning. Exemplary
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machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model development. Complete simulation verification, model validation, uncertainty quantification, and documentation
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-throughput screening, and online/in situ characterization with active-learning and Bayesian-optimization pipelines to guide experiment selection Build agentic artificial intelligence (AI) workflows and FAIR
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motivated researcher to develop a strong independent research profile at the interface of Bayesian statistics, clinical trial design, optimization, computational statistics, and/or translational cancer
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monitoring Preferred Qualifications: Experience working with any of the following: Bayesian hierarchical modeling, occupancy modeling, joint species distribution models, integration of multiple data types
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, ● Methods for heterogeneous treatment effects estimation, ● Methods for multiple exposures, multiple outcomes, ● ML and AI methods for causal inference, ● Bayesian causal inference, ● methods
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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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to high-dimensional statistics; Bayesian statistics; resampling techniques; digital twins; uncertainty quantification; foundations of machine learning and artificial intelligence; optimization theory and