The University of Cambridge, in collaboration with IBM and the Department of Engineering, is offering a PhD studentship under the EPSRC Industrial Doctoral Landscape Award (IDLA) focused on Probabilistic Numerics and Inverse Problems. This research opportunity is at the intersection of mathematical and computational modelling, targeting large-scale inverse problems governed by Partial Differential Equations (PDEs) in Earth and planetary systems. The project aims to advance the field of probabilistic numerics, where uncertainty in numerical computation is explicitly represented and quantified, providing new approaches to complex geophysical and planetary models.

IBM's practical challenges in modelling Earth and planetary systems often involve data-scarce environments where traditional Foundation-Model (FM) surrogates are not feasible. This studentship will explore synthetic data generation using direct PDE solvers and investigate how probabilistic numerical methods can enhance, supplement, or replace existing approaches. The goal is to enable more principled uncertainty quantification and improved performance in large-scale inverse modelling tasks relevant to both academic and industrial contexts.

Applicants should hold or expect to obtain a good UK Master's degree (or overseas equivalent) in a relevant science subject such as Engineering, Physics, Computer Science, or Mathematics. The ideal candidate will be self-motivated, capable of independent research, and able to communicate their findings effectively. Application materials must include a short research statement (maximum 1 page), curriculum vitae, publication list, and contact details for two referees who can provide recommendation letters. There is a £20 application fee, and early applications are encouraged as the position may be filled before the advertised deadline.

EPSRC IDLA studentships are available for eligible home students and a limited number of international students. The University of Cambridge actively supports equality, diversity, and inclusion, welcoming applications from all backgrounds. For queries, applicants may contact Professor Mark Girolami at [email protected], with a copy to [email protected].

To apply, visit the University of Cambridge Application Portal and follow the instructions to submit your documents. This is an excellent opportunity to contribute to cutting-edge research in probabilistic numerics, inverse problems, and computational modelling, with direct relevance to both academic and industrial applications.