Next-generation Bayesian inference for Earth and Space Science.

Thanks to advances in space observation, large, high-resolution datasets are now available for studying Earth’s climate, as well as the environments of Mars and the Moon. Extracting physical information from these datasets involves solving complex inverse problems that link measurements to their underlying causes. This PhD programme focuses on Bayesian methods for estimating physical parameters from high-dimensional remote sensing data. Rather than relying on traditional assumptions, it uses modern generative AI models, particularly diffusion models, to better represent priors and sample the posterior probability distribution. The work also aims to improve inference efficiency by reducing dimensionality, reusing computations, and combining multiple measurements. The PhD candidate will develop, test and validate these methods. They will then be applied by him (her) to selected case studies for mapping the surfaces of Mars, the Moon and Earth using data from current space missions.