University of Exeter funding: NERC GW4+ DTP PhD studentship

Geostatistical models for parametric insurance triggers, NERC GW4+, PhD in Mathematics studentship. Ref: 3354

About the award


Lead Supervisor

Dr Ben Youngman Department of Mathematics, College of Engineering, Mathematics and Physical Sciences University of Exeter.

Additional Supervisors

Dr Theo Economou Department of Mathematics, College of Engineering, Mathematics and Physical Sciences University of Exeter.

Geoffrey Saville, Willis Towers Watson

Location: University of Exeter, Streatham Campus, Exeter EX4 4QJ.

This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP).  The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five Research Organisation partners:  British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology, the Natural History Museum and Plymouth Marine Laboratory.  The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see

For eligible successful applicants, the studentships comprises:

  • An index-linked stipend for 3.5 years (currently £14,777 p.a. for 2018/19);
  • Payment of university tuition fees;
  • A research budget of £11,000 for an international conference, lab, field and research expenses;
  • A training budget of £4,000 for specialist training courses and expenses.


Students from EU countries who do not meet the residency requirements may still be eligible for a fees-only award but no stipend.  Applicants who are classed as International for tuition fee purposes are not eligible for funding.

Project details

Reliably quantifying and predicting natural disaster risk is vital for the exposed/vulnerable population and insurance companies. Reinsurers that underwrite such risk typically rely on catastrophe models for quantification. Parametric insurance is a cost-effective alternative to reinsurance, by avoiding development costs of catastrophe models, which gives developing countries opportunity to promptly receive financial support following natural disasters. One insurer, CCRIF SPC, paid Caribbean countries approximately $31.2 million soon after Hurricane Irma.

The rise of parametric insurance highlights its importance and the benefit of parsimonious, cost-effective modelling. Most parametric insurance products trigger a payout when simple criteria are met, e.g. a flood payout if rainfall exceeds some threshold, irrespective of whether a flood occurs. Basis risk describes the mismatch between payouts and disaster occurrence. This project’s research will aim to help minimise basis risk by building statistical models able to represent natural hazard events.

Project Aims and Methods

This project will focus on the meteorological component of natural disasters, i.e. rainfall for flooding, or wind speeds for hurricanes. It will involve the development of geostatistical models to support the parametric insurance industry. Such models allow fast, efficient and robust simulation of meteorological hazards associated with natural disasters, such as floods or hurricanes. Compared to typical vendor-operated catastrophe models, geostatistical models require significantly less computing resource and hence expense. In fact such models can be translated into open-source user-friendly computer code, which offers the industry transparency. Both sides of the industry can benefit: parametric insurers can validate and/or modify criteria to reduce basis risk, while customers can better understand and compare products. Ultimately, reinsurance could even become more cost-effective.

The majority of the project will involve the development of cutting-edge statistical methodology and accompanying software to efficiently combine geostatistical and extreme-value models for use with Big Data. This will enable fast simulation of natural disasters at high resolution without the need for supercomputation. The project will therefore produce statistical catastrophe models that are accessible to developing countries. Not only will work be on the frontier of academic research, but significant collaboration with partner Willis Towers Watson will helps the work's impact outside of academia.

CASE or Collaborative Partner
Willis Towers Watson (WTW) is a leading global advisory, broking and solutions company that helps clients around the world turn risk into a path for growth. Through the Willis Research Network (WRN), WTW continues to be a leading industry player in integrating latest science into decision-making and operational systems developed for the risk and (re)insurance sectors. The WRN was formed to forge practical links between science, policy and industry and to tackle key risks facing the global re/insurance industry. For example, WTW is one a few brokers involved in parametric insurance products. Through the WRN, Willis Towers Watson has teamed up with more than 50 world-leading institutions to develop dynamic and innovative solutions to the challenges of risk and resilience.


The successful applicant will be encouraged to attend four Academy for Postgraduate Training in Statistics (APTS) courses. Attending relevant ad-hoc courses relevant to statistics, programming, meteorology or natural hazards will also be encouraged (examples include Introduction to Catastrophe Modelling, by Oasis Loss Modelling Framework, or Big Data: tools and statistical methods, by RSS).

References / Background reading list

Youngman, B. D. & D. B. Stephenson (2016). A geostatistical extreme-value framework for fast simulation of natural hazard events. In Proc. R. Soc. A, Volume 472, pp. 0150855. The Royal Society.

Wood, E, Lamb, R, Warren, S, Hunter, N, Tawn, J, Allan, R & Laeger, S (2016) Development of large scale inland flood scenarios for disaster response planning based on spatial/temporal conditional probability analysis E3S Web of Conferences, vol 7, 01003. DOI: 10.1051/e3sconf/20160701003

Figueiredo, R. , Martina, M. L., Stephenson, D. B. and Youngman, B. D. (2018), A Probabilistic Paradigm for the Parametric Insurance of Natural Hazards. Risk Analysis. . doi:10.1111/risa.13122

Entry requirements

Applicants should have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK.   Applicants with a Lower Second Class degree will be considered if they also have Master’s degree.  Applicants with a minimum of Upper Second Class degree and significant relevant non-academic experience are encouraged to apply.

Candidate Requirements
The ideal candidate for this project will have a strong background in mathematics, and statistical modelling at undergraduate – and preferably Master’s – level. The candidate will have experience in computer programming in R, Python or similar.

All applicants would need to meet our English language requirements by the start of the  project


How to apply

In the application process you will be asked to upload several documents.  Please note our preferred format is PDF, each file named with your surname and the name of the document, eg. “Smith – CV.pdf”, “Smith – Cover Letter.pdf”, “Smith – Transcript.pdf”.

  • 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.
  • Two References (applicants are recommended to have a third academic referee, if the two academic referees are within the same department/school).

Reference information
If you do not upload your references when submitting your application, it is your responsibility to ensure that your two referees email their references to, as we will not make requests for references directly; you must either upload them with your application or arrange for them to be submitted by 29 April 2019

Please note: References should be submitted to us directly in the form of a letter. Referees must email their references to us from their institutional email accounts. We cannot accept references from personal/private email accounts, unless it is a scanned document on institutional headed paper and signed by the referee.

All application documents must be submitted in English. Certified translated copies of academic qualifications must also be provided.

The closing date for applications is midnight on 29 April 2019.  Interviews will be held week commencing 20 May 2019.

If you have any general enquiries about the application process please email  Project-specific queries should be directed to the supervisor.

Data Sharing
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.


Application deadline:29th April 2019
Value:£14,777 per annum for 2018-19
Duration of award:per year
Contact: PGR Enquiries