Joseph Grealy

I am a graduate statistician working in the air quality team at the Department for Environment, Food and Rural Affairs. My interests span environmental and climate statistics, Bayesian emulation methods, uncertainty quantification, and uncertainty-aware machine learning. I completed my MMATH at Durham University, where my dissertation developed a Gaussian Process emulator to analyse uncertainty in hydrological models across UK catchments. I have also gained experience through a UKRI research placement on soil uncertainty and an OECD internship on disaster cost estimation in Southeast Asia. Alongside methodology and applications, I enjoy building tools in R: developing reproducible pipelines, efficient parquet-based workflows, packages, and Quarto books for clear communication. I am keen to pursue a PhD focused on novel emulation techniques for large-scale climate or environmental models, bridging methodological development with environmental applications.