Funded PhD Positions in Bayesian Uncertainty Quantification at the University of Exeter

The International Society for Bayesian Analysis; Exeter; On-site; GBP 15,000 - 20,000; Full time

Job summary

The University of Exeter is offering up to 4 fully funded PhD positions for the 2019/20 entry, focusing on Bayesian Uncertainty Quantification. Candidates will work on projects involving Gaussian process emulation and Calibration of numerical models, gaining exposure to cutting-edge research in collaboration with the Alan Turing Institute.

Qualifications

• Strong background in Bayesian methods and uncertainty quantification.
• Experience with Gaussian processes is advantageous.
• Interest in applying statistics to complex models.

Responsibilities

• Engage with the Alan Turing Institute for Data Science and AI.
• Contribute to active research in Bayesian methods for Uncertainty Quantification.

Education

Master's degree or equivalent in a relevant field

Job description

Funded PhD Positions in Bayesian Uncertainty Quantification at the University of Exeter

Apr 14, 2019

The University of Exeter Mathematics department is offering up to 4 fully funded doctoral studentships for 2019/20 entry. Full details of the funding awarded, eligibility and a list of all possible projects is available here

There are two advertised projects in Bayesian Uncertainty Quantification, both co-supervised by Professor Peter Challenor and Dr Daniel Williamson. One entitled “Matching covariance kernels to numerical models in Gaussian process emulation” (full description here ), the other entitled “Calibration of numerical models with deep Gaussian processes” (full description here ). Both projects will enable the successful candidate to engage with the Alan Turing Institute for Data Science and Artificial Intelligence, and to join an active group of researchers in Bayesian methods for Uncertainty Quantification for complex models, with applications in Earth system science, human health and engineering.

Applications close on the 13th May 2019.