Thomas Newman

Background

About Thomas Newman

I’m a final-year PhD candidate in Statistics at Lancaster University, specialising in Bayesian inference, computational modelling, and machine learning. My research sits at the intersection of statistical theory and environmental impact, developing scalable MCMC methods and deep-learning surrogates to solve complex inverse problems in computational fluid dynamics. Working in collaboration with Shell and the STOR-i Centre for Doctoral Training, I’ve focused on detecting and quantifying greenhouse gas emissions by modelling spatio-temporal gas dispersion and uncertainty. My work aims to bridge cutting-edge statistical tools with real-world challenge - delivering insights that support data-driven climate solutions. I'm especially interested in applying my expertise to research-oriented data science roles, where I can develop and deploy statistical or ML models to tackle meaningful, high-impact problems across sectors. Originally from France, I bring a strong sense of teamwork and leadership, honed through years of competitive sport - including captaining the Monaco rugby team and competing nationally in athletics.