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Lanterna in the School of Chemistry, University of Nottingham. The project focuses on Mapping Photocatalysts using Tandem Super-resolution Electron Microscopy and Spectroscopy in collaboration with
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/2025 Role Description An exciting opportunity for an established researcher or a recently completed PhD researcher with experience in malacology, epidemiology, data mapping and/or schistosomiasis
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mapping to join our team developing decision support systems for improved schistosomiasis control during water infrastructure projects. About the Role Schistosomiasis often spreads in areas with water
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to £41,478 per annum. An exciting opportunity for an established researcher or a recently completed PhD researcher with experience in malacology, epidemiology, data mapping and/or schistosomiasis modelling and
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modelling, satellite data assimilation, multivariate statistics, and machine learning. Prior experience with model and satellite products for mapping and understanding SM-dependent hazards (like floods
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modelling, satellite data assimilation, multivariate statistics, and machine learning. Prior experience with model and satellite products for mapping and understanding SM-dependent hazards (like floods
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collaboration that has mapped emotional hotspots in four cities in the UK and EU using spatial analysis of social media data. The next phase of the project aims to incorporate explainable AI (XAI) to interpret
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currently exists to map and explain the diverse mechanisms of housing exclusion. Addressing this gap requires integrating geospatial analysis, explainable AI (XAI), and behavioural research methods, including
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behavioural insights have been used to study urban inequalities, no systematic global effort currently exists to map and explain the diverse mechanisms of housing exclusion. Addressing this gap requires
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collaboration that has mapped emotional hotspots in four cities in the UK and EU using spatial analysis of social media data. The next phase of the project aims to incorporate explainable AI (XAI) to interpret