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models and Bayesian approaches to tackle complex, real-world data? Join this PhD project to build dynamic models and study cognitive variability using ecological momentary assessment (EMA). Join us We are
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., stochastic gradient methods and Bayesian learning), Probabilistic performance guarantees, leveraging tools from stochastic systems, RKHS-based learning, and Bayesian inference to certify performance and
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optimization-based updates (e.g., stochastic gradient methods and Bayesian learning), Probabilistic performance guarantees, leveraging tools from stochastic systems, RKHS-based learning, and Bayesian inference
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networks, Bayesian neural networks, conformal prediction intervals and generative AI for synthetic data generation. You will also develop frameworks for uncertainty quantification in forecasting and
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partners. In addition, you (co-)lead national-scale applied research projects, for instance by taking a leading role on some scientific objectives of one of SoDa’s major research projects (Macroscope project
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and geometric deep learning, or simulation-based inference. We welcome your unique perspective and are eager to learn how your track record, educational vision, and future research goals align with
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, methodologies, and information derived from Bayesian modeling, data science, cognitive science, and risk analysis. Its primary objective is to create advanced forecasting models, generate meaningful indicators
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on some scientific objectives of one of SoDa’s major research projects (Macroscope project, funded by NWO). As our new colleague, you also engage in teaching in methodology, statistics and data science
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. mixed effects regression models and/or Bayesian statistics; e.g., brms / lme4 packages). Excellent written and spoken English. Desirables (traits that would give you an advantage) Training in evolutionary
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observational data, and the application of advanced methods for longitudinal and prediction modelling. You will also conduct methodological research on Bayesian methods and other innovative methodology