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Exactly: A Bayesian Approach. The project aims to address the challenges in pooling inference, by developing and implementing either exact or asymptotically exact Monte Carlo algorithms in collaboration
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opportunity for a highly motivated and skilled Research Associate/Assistant in statistics to join the EPSRC funded project PINCODE: Pooling INference and COmbining Distributions Exactly: A Bayesian Approach
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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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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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for differentiating effectful programs such as gradient estimation of probabilistic programs, implicit function differentiation, compositional Bayesian inference techniques); analyzing what is required (e.g., choice
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will be grounded in rigorous mathematics coupled with a sound understanding of the underlying earthworm ecology. Bayesian inference methodologies will be developed to estimate where and when behavioural
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and manipulating complex data structures, Bayesian modeling, analyzing nested longitudinal data, and who are familiar with techniques for handling challenging data (e.g., highly non-normal distributions
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function differentiation, compositional Bayesian inference techniques); analyzing what is required (e.g., choice of data structures, static analyses and compiler optimizations, parallelism and concurrency
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more information for a given experimental budget. Efficient active learning depends on the careful co-design of experiments and inference algorithms. You will explore topics such as how to elicit
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processes related to carbon cycling in the soil-plant system Experience with Bayesian inference and machine learning is an asset Ability to work independently and cooperatively as part of an interdisciplinary