Uncertainty Quantification and Decision Support for Complex Model Chains - Mathematics - EPSRC DTP funded PhD Studentship Ref: 2907

About the award

This project is one of a number funded by the Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnership to commence in September 2018. This project is in direct competition with others for funding; the projects which receive the best applicants will be awarded the funding.

The studentships will provide funding for a stipend which is currently £14,553 per annum for 2017-2018. It will provide research costs and UK/EU tuition fees at Research Council UK rates for 42 months (3.5 years) for full-time students, pro rata for part-time students.

Please note that of the total number of projects within the competition, up to 15 studentships will be filled.

Supervisors
Dr Daniel Williamson

Professor Peter Challenor

Location
Streatham Campus, Exeter

Project Description
Complex and computationally expensive numerical models are used to help address some of the most important challenges faced by society, business and policy makers today. Uncertainty quantification (UQ) is an area of research within statistics that aims to develop methods for understanding what these models can tell us about the real world in order to assist decision making under uncertainty. In particular, many of the input parameters for these models are uncertain, leading to uncertain model output that can be combined using statistical models with observations and further structural understanding to say something about reality for decision makers.

For complex problems, often models must be combined in chains. For example, a global climate model under a future CO2 profile gives output that can be used as the input to a regional climate model. The outputs of this model can be used to drive rain runoff models and water management models that are needed to help decision makers in water planning. When standard UQ methods are applied naively to model chains, the uncertainty cascades and blooms so that, at the end of the chain, where the decision lies, the uncertainty is too great to be useful to decision makers. This project will develop new methods to restrict this uncertainty bloom. The approach will involve first reducing the number of inputs across the chain to those considered to be “decision critical” and the first phase of the PhD will develop methods for identifying decision critical elements of the chain. The next phase will look to constrain the uncertainty of these chain elements specifically using observations and Bayesian calibration methods. The final phase, having quantified uncertainty will look to develop decision support tools for model chains. 

The methods will be developed using a model chain provided by Dstl for responding to chemical and biological hazards linking: a hazard plume model, a meteorological model, models for building ventiliation and exposure risk models in order to help decide how to respond to the releasing of such a hazard (e.g. evacuate and in which direction, or remain in place). 

The successful applicant will join a world-leading group in uncertainty quantification comprising 10 researchers, including academics, postdoctoral researchers and PhD students. 
 

Entry Requirements

You should have or expect to achieve at least a 2:1 Honours degree, or equivalent, in mathematics (or equivalent) with a substantial statistical component. Experience in programming with R and with Gaussian processes is desirable.

If English is not your first language you will need to meet the English language requirements and provide proof of proficiency. Click here for more information and a list of acceptable alternative tests.

The majority of the studentships are available for applicants who are ordinarily resident in the UK and are classed as UK/EU for tuition fee purposes.  If you have not resided in the UK for at least 3 years prior to the start of the studentship, you are not eligible for a maintenance allowance so you would need an alternative source of funding for living costs. To be eligible for fees-only funding you must be ordinarily resident in a member state of the EU.  For information on EPSRC residency criteria click here.

Applicants who are classed as International for tuition fee purposes are NOT eligible for funding. International students interested in studying at the University of Exeter should search our funding database for alternative options.

Summary

Application deadline:10th January 2018
Value:3.5 year studentship: UK/EU tuition fees and an annual maintenance allowance at current Research Council rate. Current rate of £14,553 per year.
Duration of award:per year
Contact: Doctoral Collegepgrenquiries@exeter.ac.uk

How to apply

You will be required to upload the following documents:
•       CV
•       Letter of application outlining your academic interests, prior research experience and reasons for wishing to
        undertake the project.
•       Transcript(s) giving full details of subjects studied and grades/marks obtained.  This should be an interim
        transcript if you are still studying.
•       If you are not a national of a majority English-speaking country you will need to submit evidence of your current
        proficiency in English.  For further details of the University’s English language requirements please see
        http://www.exeter.ac.uk/postgraduate/apply/english/.

The closing date for applications is midnight (GMT) on Wednesday 10 January 2018.  Interviews will be held at the University of Exeter in late February 2018.

If you have any general enquiries about the application process please email: pgrenquiries@exeter.ac.uk.
Project-specific queries should be directed to the supervisor.

During the application process, the University may need to make certain disclosures of your personal data to third parties to be able to administer your application, carry out interviews and select candidates.  These are not limited to, but may include disclosures to:

• the selection panel and/or management board or equivalent of the relevant programme, which is likely to include staff from one or more other HEIs;
• administrative staff at one or more other HEIs participating in the relevant programme.

Such disclosures will always be kept to the minimum amount of personal data required for the specific purpose. Your sensitive personal data (relating to disability and race/ethnicity) will not be disclosed without your explicit consent.