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and carbon cycle model-data integration using the CARDAMOM Carbon-Water Bayesian model-data integration framework. The candidate will help advance global land biosphere estimates of biomass, water
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structures, Bayesian approaches are proposed along with the supersaturated and D-optimal designs in the literature. This project aims to explore the current literature on Bayesian supersaturated D-optimal
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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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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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, 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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The relationship between the information-theoretic Bayesian minimum message length (MML) principle and the notion of Solomonoff-Kolmogorov complexity from algorithmic information theory (Wallace and
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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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plants they visit and pollinate. Bayesian networks (BNs), and other probabilistic graphical models, can provide a visual representation of the underlying structure of a complex system by representing