The research will focus on developing forward models governed by coupled radiative and heat transfer PDEs, while exploring alternative, more informative measurement modalities to significantly boost spatial resolution and noise robustness under severe data scarcity. To address the chaotic nature of dynamic flows, you will construct Bayesian inference frameworks and random media models to parameterise chaos and achieve data-consistent uncertainty quantification. Furthermore, the project offers scope to integrate scientific machine learning, such as neural operators and diffusion models, to efficiently represent complex, swirling fluid structures alongside classical physics-based inversions. Bridging deep mathematical theory with real-world applications in aerospace propulsion, combustion efficiency, and environmental monitoring, this project involves direct collaboration with specialists in gas metrology and aerospace engineering to validate computational models against experimental data.
It is ideal for ambitious candidates in Engineering, Applied Mathematics, Computational Physics, or Signal Processing with a strong foundation in differential equations, linear algebra, inverse problems, or statistical learning.
Eligibility
Funding Notes
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.
Further information and other funding options.
Funding may become available on a competitive basis.
