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Field
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model fitting, including Bayesian model fitting. Experience of management and analysis of large multidimensional real world data sets. What we can offer you The opportunity to continue your career at a
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, or other relevant analytical software. • Knowledgeable of Bayesian statistical methods, numerical modeling methods, and other complex quantitative analytical methods. • Experience with open science practices
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, sampling, inference, and machine learning. On one side, statistical approaches such as Bayesian inference play a critical role in identifying the parameters of PDEs, while on the other, newly emerging
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projects, including: The post-holder will run numerical models that simulate the dispersion of greenhouse gases through the atmosphere. These models will be used, in Bayesian inference frameworks
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international projects, including: The post-holder will run numerical models that simulate the dispersion of greenhouse gases through the atmosphere. These models will be used, in Bayesian inference frameworks
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disease data. A collaborative mindset, excellent communication skills, and the ability to work across disciplines. (Desirable) Familiarity with malaria epidemiology, Bayesian methods, or international
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Research Associate to contribute to a project focused on robust Bayesian inference with possibility theory. Robust inference is crucial for many real applications in which datasets are invariably corrupted
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Bayesian statistics, statistical computing, spatial statistics, experimental design, and survival analysis. Faculty are active in application areas of neuroscience, geology, biostatistics, psychometrics
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at Stockholm University. We have a strong tradition in sampling but areas that we are growing in include, but are not limited to, Bayesian inference, the intersection of statistics and machine learning
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. Please direct all questions about the position to Dr. Jessica Jaynes at jjaynes@fullerton.edu . Statistics at CSU Fullerton The statistics faculty research areas include Bayesian statistics, statistical