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with cancer. Proven ability to develop novel computational methodologies, including Bayesian generative models, phylogenetic inference tools, and algorithms for multimodal data integration. Demonstrated
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advanced statistical methodologies, including several of the following: Survival analysis Hierarchical and mixed-effects models Clinical trial design and analysis Structural equation modeling Bayesian data
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Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field observations—including
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is required Desired qualifications: Experience with data assimilation, probabilistic machine learning, Bayesian inference, inverse modeling, and/or simulation-based inference is an advantage
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data, epidemiologic modeling, Bayesian analysis, modern causal inference, statistical genetics and genomics, machine learning methods, health economics, survey design, systematic reviews, behavioral
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requirements, is provided below and in the links. Project descriptions Project 1: Predictive Bayesian inference and foundation models Employment: University of Oslo, Department of Mathematics PhD programme