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comparing models with entirely different structures and parameter counts, whether comparing linear regression against mixture models or decision trees. MML is strictly Bayesian, requiring prior distributions
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have recently demonstrated its use in a Bayesian framework for evaluating South American methane emissions. In this role, you will continue the development of GATES under two grants from the Natural
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-2392 ). We have recently demonstrated its use in a Bayesian framework for evaluating South American methane emissions. In this role, you will continue the development of GATES under two grants from
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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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for applying proteomics and genetics data collected in situ for integrative structure modeling. Critical aspects of the research include: (1) Designing and executing methods to integrate data from different
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implementing models that integrate ecological dynamics, species traits, phylogenetic trees, and economic discounting; ● Devising Bayesian or POMDP frameworks to handle uncertainty about species interactions
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also develop predictive models capable of characterising disease progression and forecasting individual outcomes in multiple sclerosis under different treatment exposures. You will contribute to delivery
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multiple sclerosis under different treatment exposures. You will contribute to delivery of collaborative projects, working closely with clinicians, imaging experts, and computational scientists across
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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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opportunity to develop and apply new AI algorithms for science. Our group also welcomes new research directions, and collaboration across different groups at the laboratory and beyond. We publish in peer