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person. Aims: The project aims to develop and evaluate AI methods for medical image analysis to detect diabetic retinopathy, glaucoma, cataract and age-related macular degeneration (AMD). As
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, encompassing advanced geospatial analysis, remote sensing methods, atmospheric transport modelling, and epidemiological data integration. The researcher will also receive guidance in handling large datasets
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skills, which will be required to lead the development of automated data analysis and modelling. The student will also undertake a research secondment to coral reef monitoring sites at NEOM for three
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will apply nonlinear and associational (colloquially called “causal”) timeseries analysis techniques to provide a more rigorous, and more statistically significant framework for understanding
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and morphological properties of the nanoengineered graphene. Electrochemical analysis using cyclic voltammetry (CV) and fast-scan cyclic voltammetry (FSCV) will determine sensitivity and redox behavior
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expand current technology to include automated live analysis, integrating machine learning algorithms capable of interpreting the complex behavioural patterns of mussels in response to environmental stress
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. The key objectives are: Objective 1: Identify optimal FPV sites in Cornwall and the South-West using GIS and multicriteria analysis to maximise green hydrogen potential, while evaluating legal frameworks
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dynamics and, if appropriate, field work (Nicholas, Aalto); numerical modelling (Nicholas, Hawker); machine learning (Hawker, Aalto); and analysis of remote sensing (Aalto) and population datasets (Hawker
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opportunity to devise an exciting research project, to receive training in data capture and manipulation, statistics, trait analysis, and modelling of interaction webs, and to undertake fieldwork
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, species distribution modelling, stomach content and dietary isotope analysis, and ecosystem modelling. Useful recruitment links: For information relating to the research project please contact the lead