Hector Altamirano-Medina

UCL is offering a PhD project in data-driven housing risk modelling for healthier homes at the Great Ormond Street Institute of Child Health. The research will use machine learning to predict damp and mould risk across the English housing stock, drawing on datasets such as the English Housing Survey. The project will integrate dwelling and household characteristics, climate change scenarios and neighbourhood factors to develop robust, explainable and transferable prediction models. Models will be validated across multiple datasets, including the UK Longitudinal Linkage Collaboration, a secure platform linking longitudinal population studies with administrative health, housing and environmental data. Research outputs will include novel machine learning methods and analytical tools for housing risk assessment, together with evidence on the health impacts of poor indoor environments. The supervisory team comprises Professors Pia Hardelid, Anna Mavrogianni, Hector Altamirano-Medina and Jessica Sheringham, with expertise spanning architectural engineering, building physics, statistics and public health. The project will also involve collaboration with local authorities and housing organisations. Applicants should have a strong interest in public health, an MSc in engineering, statistics, data science, epidemiology or a related discipline, strong programming skills, and experience with machine learning and large, complex datasets. The project is open only to Home fees candidates and is linked to the UCL EPSRC Doctoral Landscape Award (UELA) Studentships. The application deadline is 8 January 2027 .