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PhD Studentship - Effective Emulation of Numerical Simulators, with Application to Tsunami Modelling

University College London

Qualification Type: PhD
Location: London
Funding for: UK Students, EU Students, International Students
Funding amount: £17,009
Hours: Full Time
Placed On: 15th July 2019
Closes: 1st September 2019
Reference: 1816401
 

Start Date: 23 September 2019, or shortly thereafter

Applications are invited for a PhD funding opportunity in the UCL Department of Statistical Science, available from September 2019. The studentship will be 3 years in duration and covers tuition fees up to the overseas rate, plus an annual stipend (£17,009 in 2019/20). This funding is provided by the Lloyd's Tercentenary Research Foundation, the Lighthill Risk Network and the Alan Turing Institute.

Studentship Information

Uncertainty Quantification (UQ) techniques help propagate uncertainties from inputs to outputs in complex simulators, such as climate or tsunami computer models that run on supercomputers. Typically UQ makes use of surrogate models - also known as emulators - that are much faster to run than simulators, in order to sample uncertainties efficiently. These are often Gaussian Process (GP) emulators that need to be fitted using a smart design of computer experiments. However, building efficient GPs and making many predictions is still a challenge in many practical settings.

This PhD project includes hardware acceleration of GP fitting and prediction in collaboration with Warwick University and the Research Software Engineering team of the Alan Turing Institute, and is linked to the project: Uncertainty quantification of multi-scale and multi-physics computer models. As part of this PhD project, it will be also possible to explore new extensions of GP surrogate models. The project includes an application to tsunami modelling that will be carried out as part of an international project on Indonesian tsunamis with various experts providing support and data.

Person Specification

The requirement for admission to the MPhil/PhD in Statistical Science is a 1st class or high upper 2nd class Bachelor’s degree, or a Master’s degree with merit or distinction, in Mathematics, Statistics, Computer Science, or a related quantitative discipline. Overseas qualifications of an equivalent standard are also acceptable. Further details can be found on the Departmental website.

The ideal candidate will have both statistical and computational expertise, for instance through a Master degree in Computational Statistics, Data Science or equivalent. Informal enquiries to Professor Serge Guillas are welcome.

How to Apply

For details on how to apply, please visit: atsv7.wcn.co.uk/search_engine/jobs.cgi?owner=5041394&ownertype=fair&jcode=1816401

Applications will be considered on a rolling basis, the first batch on 01 August, until the studentship is filled (i.e. the below closing date represents only a final deadline). You are therefore advised to apply as soon as possible.

   
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