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. Yet, many stellar and planetary parameters remain systematically uncertain due to limitations in stellar modelling and data interpretation. This PhD project will develop Bayesian Hierarchical Models
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the structure and robustness of the ecological networks supporting reef fish communities at different positions along depth, latitude, and longitude gradients; challenging these networks under hypothesized future
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depth-related plasticity in the physiology and behaviour of mesophotic reef fish. Identifying depth-related changes in the structure and robustness of the ecological networks supporting reef fish
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-supervised learning, and few/zero-shot techniques — the student will adapt models to ecological data. Bayesian deep learning and ensemble methods will be explored for trustworthy uncertainty estimation
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health. You will develop and apply cutting-edge machine-learning techniques to identify the most informative indicators of ecosystem change and use them to build dynamic Bayesian network (DBN) ecosystem