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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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and artificial intelligence. You will play a key role in coordinating research data, developing and evaluating AI models, analysing complex imaging datasets and contributing to the dissemination
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measurements and additional data. Specifically, the project’s objective is to develop a data-driven method that maps satellite-derived data to ground-level concentrations for air pollutants such as NO2 (nitrogen
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combines large language models, real-world data, behavioral science, and theory-driven modeling to build realistic and interpretable simulations for emergency planning and risk communication. The student
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, to study how machine learning models and neural circuits in the brain process states of emotion. The Nair group combines cutting-edge experimental tools with the development of new AI methods to understand
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measurements and additional data. Specifically, the project’s objective is to develop a data-driven method that maps satellite-derived data to ground-level concentrations for air pollutants such as NO2 (nitrogen
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cellular and animal models of cardiopulmonary disease and performing molecular biology, cell biology, histology, microscopy, and physiological experiments. • Design a method to isolate pulmonary vascular
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, track, or present information clearly. Essential Functions Develop and run simulations using computational modeling tools Conduct parametric studies by modifying input conditions (e.g., furnace wall
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and screen genetically encoded peptide libraries in cancer cell models, with the aim of identifying molecules and mechanisms that influence cancer cell behaviour. The role offers an excellent