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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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activities are focused on the modelling of the mechanics of structures, components, and processes along with involved materials, thereby providing mechanics-based solutions and virtual solution tools to our
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practices. Within this framework you will: extend and use a process-based modeling approach which explicitly represents microorganisms and biomolecule functioning in soil systems. use process-based modeling
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needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project
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the solution of governing PDEs. - Train machine learning models to predict lifetime and failure based on loading and environmental histories. The PhD student will have access to world-class computing facilities