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3.5-year D.Phil. studentship Supervisor: Dr Thiago B. Burghi This project will develop data-driven models for predicting seizure-like events in living neural networks. Such events can be induced in
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geochemical alteration occurs, and the associated uncertainty in predicting reservoir behaviour. This will be done with laboratory investigations and geochemical modelling. Specifically, you will: Characterise
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, which charts how cognitive disorders, pharmacological interventions, and brain stimulation reposition the brain within a shared functional space, revealing principles for prediction and control. We
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To make reliable predictions and analyses of air pollution, data assimilation methods, which integrate observational data into model simulation to update the model state and the underlying
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Modeling” division at ICE-3 aims to model and predict the composition of the atmosphere. The research focuses on multiphase processes, the role of tropospheric oxidants, and the role of pollutants in
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, documentation, and simulations, sustainability assessments, etc. Research areas include: • Advance data science, predictive modelling, and intelligent systems for the Agro-Food domain. • Adopt theoretical
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develop research to understand predictive capabilities from ocean temperatures and salinities on weather and climate in New Zealand. The project would utilise existing data sets, new ocean reanalyses and