13 learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions at University of Exeter in United Kingdom
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predicts something important? This project will uncover the neural circuits that transform visual information into dopamine signals that teach the brain about motivation, action and decision-making
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and machine learning methodology to help deal with key challenges in developing such models in large-scale observational electronic healthcare record data. These models will be applied to important real
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The always-on, safety-critical nature of air traffic control raises rich and exciting challenges for machine learning and AI. The University of Exeter in partnership with NATS, the UK’s main air
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The continuous release of Earth Observation (satellite) data and the emergence of Machine Learning methods open up new possibilities for understanding forests. These large datasets provide
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environments where terrain can change abruptly. However, formally certifying these opaque learning-based components demands impractical resources, presenting critical safety assurance challenges and delaying
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individual expressive behaviours. The project builds on recent work showing that person-specific cognition can be computationally approximated by learning personalised neural architectures or model weights
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knowledge graph from scientific papers and cognitive test questionnaire data, and second, to integrate the graph with transformer-based large language models and causal learning. This offers an explainable
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on clinical complications, and use machine learning to develop and validate predictive models to identify high-risk patients. The research aims to individualise inpatient care, reduce hospital-acquired
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for place with the projects across the MRC GW4 BioMed3 Doctoral Landscape Award studentships. There are a total of 18 studentships available across the partnership. How do neurons instruct astrocytes
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individual expressive behaviours. The project builds on recent work showing that person-specific cognition can be computationally approximated by learning personalised neural architectures or model weights