Big computations and big data = robust results?


Contact Dr John Paul Gosling to discuss this project further informally.

Project description

Analyses involving large quantities of data or complicated computer models are the norm nowadays across most of science. These analyses can be computationally expensive and require many assumptions to make them feasible in a reasonable amount of time. As such, analyses are rarely repeated and little is done to check the robustness of the results to deviations from the underpinning assumptions.

The proposed project follows on from "A Bayesian computer model analysis of Robust Bayesian analyses" by Vernon and Gosling. Here we utilised emulation technology to allow practitioners to check the robustness of their analyses and to highlight aspects of the analysis that are sensitive to changes in the assumptions.

This project will extend this work and demonstrate the method's utility for several real examples from climate science to toxicology.

Entry requirements

Applicants should have, or expect to obtain, a minimum of a UK upper second class honours degree in Mathematics or a related discipline, or equivalent.

If English is not your first language, you must provide evidence that you meet the University's minimum English Language requirements.

How to apply

Formal applications for research degree study should be made online through the university's website. Please state clearly in the research information section that the PhD you wish to be considered for is 'Big computations and big data = robust results?' as well as Dr John Paul Gosling as your proposed supervisor.

We welcome scholarship applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.

If you require any further information please contact the Graduate School Office, e: