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of inverse problems, Bayesian learning, and uncertainty quantification. The specific project will be tailored to your expertise and interests; examples include: Efficient inference techniques for high
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environmental factors such as fluctuating wind speeds and saltwater exposure. Using advanced statistical and machine learning techniques, including Bayesian inference and stochastic modelling, the project will
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Science, Telecommunications, Applied Mathematics, or related fields; Solid background in probabilistic modeling, Bayesian inference, information theory, and/or machine learning; Experience with signal processing or decision
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statistical and machine learning techniques, including Bayesian inference and stochastic modelling, the project will quantify and analyse uncertainties in the design and operational performance
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, etc.) development of predictive models and digital decision-support tools for nutrition and health method development in causal inference, integration of heterogeneous data sources, uncertainty
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programming techniques (e.g., techniques for differentiating effectful programs such as gradient estimation of probabilistic programs, implicit function differentiation, compositional Bayesian inference