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carbon, water and energy states. The successful applicant will specifically support carbon and water cycle science, applications and process model innovations using CARDAMOM-based Bayesian inference
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in knowledge-informed machine learning. The ideal candidate will have a strong background in developing and integrating probabilistic graphical models, Bayesian networks, causal inference, Markov
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to statistical computing, Bayesian modeling, causal inference, clinical trials and analysis of complex large-scale data such as omics data, wearable tech, and electronic health record, with specific preference
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to statistical computing, Bayesian modeling, causal inference, clinical trials and analysis of complex large-scale data such as omics data, wearable tech, and electronic health record, with specific preference
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Bayesian approach (Lages, 2024). Techniques used: Computational modelling, Bayesian inference, sampling and simulation techniques, prior distributions and posterior predictive checks, model comparison