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for inference, yet differs from standard Bayesian approaches through its information-theoretic foundation. The MML87 approximation achieves computational tractability while remaining virtually identical to Strict
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to the development of Bayesian inference frameworks that use GATES. The postholder will develop machine learning models of atmospheric transport and use them in Bayesian inverse modelling frameworks to estimate
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and Boulton, 1968; Wallace and Dowe, 1999a; Wallace, 2005) is a Bayesian information-theoretic principle in machine learning, statistics and data science. MML can be thought of in different ways - it
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, optimization, dynamic systems, decision theory, Bayesian inference) ● Is motivated to apply these methods to ecological, evolutionary, and conservation systems; ● Is comfortable with uncertainty, modeling
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) Uncertainty quantification, 4) Model interpretability. Experience with other deep learning methods, such as Convolutional or Bayesian Neural Networks, Simulation-Based Inference (SBI), Normalizing Flows
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, Bayesian inference and interrogation theory. The post may involve travel to Iceland and Italy in support of your work and attendance at international conferences, such as the European Geothermal Congress
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close to the nest [1 ] but to better understand foraging, we need landscape level detail. The direction of the project can be tailored, but could include developing and applying Bayesian ML approaches
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and learned surrogates with clear statistical validation; Bayesian inverse problems and data assimilation via measure transport and amortized inference; robustness and distribution shift in scientific
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Bayesian ML approaches for path inference; introducing sensors; behaviour classification; resource-constrained active-learning; other IoT applications; microbattery development and field experiments and
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transferring learning from other geographic regions and data types, machine learning methods, Bayesian inference and interrogation theory. The post may involve travel to Iceland and Italy in support of your work