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. Supervisor Bio I’ve been working in the field of Bayesian ML for 10+ years, with a focus on real-world applications and have developed several insect tracking techniques. As well as an opportunity to conduct
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and learned surrogates with clear statistical validation; Bayesian inverse problems and data assimilation via measure transport and amortized inference; robustness and distribution shift in scientific
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Bayesian ML approaches for path inference; introducing sensors; behaviour classification; resource-constrained active-learning; other IoT applications; microbattery development and field experiments and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 6 days ago
of Biostatistics. Specifically, the position works on and provides oversight to several federal and industry research and training grants in the areas of casual inference, Bayesian methods, robust methods, frailty
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 16 hours ago
of Biostatistics. Specifically, the position works on and provides oversight to several federal and industry research and training grants in the areas of casual inference, Bayesian methods, robust methods, frailty
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and applied mathematics, statistics, and math education. We are seeking a tenure-track Assistant Professor to join our statistics group and contribute to its growth and success. We invite applications
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version control and containerization (Docker/Singularity) Statistical Modeling: Quantitative data analysis using GLMs, Bayesian methods, or mixed-effect models to interpret complex perturbation datasets
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and statistics, with expertise spanning time series analysis, Bayesian inference, financial econometrics, and data analytics. As home to one of the strongest forecasting research groups worldwide, we
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, kernel machines, decision trees and forests, neural networks, boosting and model aggregation, Bayesian inference and model selection, and variational inference. Practical and theoretical understanding