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Experience developing pipelines and code for gravitational-wave searches and/or parameter estimation Knowledge of advanced Bayesian methods and samplers, machine learning approaches to signal processing
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, demographic modelling, Bayesian hierarchical models and/or modelling with multiple data streams • Experience with data science and biodiversity informatics, in particular handling of scientific collection
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research with young children Experience with computational methods (e.g., Bayesian modeling, drift diffusion modeling, etc.) Equipment Utilized Physical Demands and Work Environment Overview Statement
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, demographic modelling, Bayesian hierarchical models and/or modelling with multiple data streams • Experience with data science and biodiversity informatics, in particular handling of scientific collection
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through to large-scale individual-based simulation as well as statistics and Bayesian inference. This highly motivated, collaborative research group leads funded, international consortia in modelling, NTDs
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by combining all available data, taking advantage of the varying temporal resolution and different time spans that the records cover. This work will involve Bayesian tools developed by our research
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candidates with strong expertise in Bayesian methods, uncertainty quantification, and/or machine learning applied to nuclear theory. The group’s research spans a wide range of topics including nuclear
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Statistics or Mette Olufsen or Kevin Flores from Mathematics. Applicants with experience in Bayesian modeling, spatial statistics, mathematical modeling, data integration, uncertainty quantification and/or
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descent, random forests, etc.) and deep neural network architectures (ResNet and Transformers). Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other
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project is to develop scalable and privacy-preserving Bayesian computational algorithms. The position is intended for two to three years, with an initial one-year appointment renewable contingent upon