Exploring Low Frequency Structure in Fluid Dynamics and Numerical Analysis - Mathematics - EPSRC DTP funded PhD Studentship Ref: 2914

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.

Prof. Beth Wingate

Streatham Campus, Exeter

Project Description
This project provides funding for a 3.5 year EPSRC Phd studentship to explore the formation and stability of low-frequency structures in equations used to model problems such as weather & climate prediction and Earth’s magnetic field.

As an example of the phenomenon addressed in this studentship, consider layer formation in the Arctic Ocean.   These layers form between the cold top layer, and the warmer lower layer of the ocean and are thought to prevent the heat from the deep from melting the sea ice. Observations of these layers show they have horizontal length scales of thousands of kilometers, they are very stable and long-lived. Questions that remain unanswered are: Why are the layers so long-lived and stable? If the upper layer warms under climate change, will the number of layers decrease? If so, will this cause a run-away melting event?

The methods we will use for this exploration build on recent results of the supervisor that use a semi-group operator to transform the relevant partial differential equations into the space of their linear solutions, thereby exposing the frequency content to modelling and analysis. These results include: 1) recently developed wave-mean flow theory and 2) recent results that use this theory to develop and analyse new numerical algorithms.  The student will use both theory and computations to explore the mathematics in idealized settings.

The ideal candidate for this studentship will be someone with a degree in mathematics or physics, or related fields who has studied partial differential equations and has some experience with python, matlab, or other programming languages.

The student will be expected to learn and apply new mathematical and numerical techniques, learn about different physical applications, write computer code to conduct simulations of the phenomenon, travel to local and international conferences.

Please include keywords and phrases for which prospective applicants may be searching. We know that funding is the main barrier to study which is reflected in the search volumes, so including additional references to ‘PhD funding’; ‘PhD scholarship’; ‘PhD studentship’ would be of benefit, as well as any other broader potential related search terms.

Entry Requirements
You should have or expect to achieve at least a 2:1 Honours degree, or equivalent, in [mathematics, physics, or environmental science]. Experience in [partial differential equations, computing, fluid dynamics] 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.


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

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.