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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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techniques that are useful for the modelling of many real-life systems. These include the development and analysis of stochastic models, computer simulations, differential equations, statistical inference
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; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine
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behaviour using computational approaches such as Bayesian program synthesis and inverse reinforcement learning. Investigate the diversity of motor commands that could implement observed behaviours and explore
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the weblike distribution of galaxies in large galaxy redshift surveys, with the corresponding velocity flows inferred from measured galaxy peculiar velocities. These measurements are to be converted into a
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the corresponding velocity flows inferred from measured galaxy peculiar velocities. These measurements are to be converted into a multiscale analysis of the mass distribution, as well as that of the flow field
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optimization of batteries against the swelling phenomenon. This project aims at developing scientific machine learning approaches based on the Bayesian paradigm and electrochemical-thermomechanical models in
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Bayesian inference framework for identifying complex aerospace systems combining with limited experimental data. It can be also used to quantify uncertainties from experimental testing, significantly