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About the Project

This project concerns the development of Markov chain Monte Carlo and sequential Monte Carlo methods for unbiased estimation in Bayesian inverse problems. Inverse problems typically involve inferring the initial condition of PDEs/ODEs on the basis of real data, naturally encapsulated by a Bayesian posterior distribution. Intrinsically, there is often a numerical solution of the PDE/ODE leading to bias in the posterior inference. In this project, we will develop, with only access to numerical solutions of the PDEs, Monte Carlo based methods which can remove the bias and be effective computational tools for inference. This project will develop skills in numerical analysis, probability, statistics and programming and leave the graduating student with an excellent skill set allowing him/her to progress further in academia or to find high quality jobs in industry.

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Funding Notes

At KAUST, every admitted student is granted the KAUST Fellowship-giving you an unparalleled opportunity to enroll in any of our renowned graduate programs tuition free. You can live on the shores of the Red Sea; conduct groundbreaking research in our state-of-the-art research centers; and receive unsurpassed support through our living stipend, generous scholarship, and extensive academic community.

Every admitted student earns the KAUST Fellowship, which grants them:

· - Full Free Tuition Support
· - Monthly Living Allowance
· - Housing
· - Medical and dental coverage
· - Relocation Support

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