Using Cutting-Edge Statistical and Data Science Techniques for Optimising and Improving Weather Forecasts - Mathematics - EPSRC DTP funded PhD Studentship Ref: 2906

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 Theo Economou
Henry Odbert and Alasdair Skea

Location
Streatham Campus, Exeter.

Project Description
The importance of weather forecasting cannot be overstated: it has impacts on a range of decision making, from taking an umbrella to whether flights should be cancelled or areas evacuated. It forms an important area of research which brings together disciplines such as mathematics, statistics, data science and physics. 

Accurate and reliable weather forecasts are valuable, but in practice they rely on imperfect information. Mathematical forecasting models based on physics make approximations in order to forecast weather, while past and current weather data cannot be measured accurately, if at all. In addition, there many competing forecasting models (systems) making the task even more challenging. This PhD studentship will focus on investigating and developing statistical and data science techniques to optimally blend all sources of information available for forecasting weather, while at the same time quantifying all sources of uncertainty and error. 

The project will look at a range of methods for achieving the goal of optimally merging various data sources. These include (but are not limited to) statistical modelling techniques such as hierarchical Bayesian modelling, Bayesian melding and machine learning algorithms. An added challenge to operational weather forecasting is the need for any such technique to be practical and computationally efficient. Therefore much of the effort will be on the optimal implementation of the developed techniques on the computer. Emphasis will also be on defining suitable metrics with which to compare the developed techniques with each other as well are with current “baseline” approaches.

This project offers a unique opportunity for a student to gain experience in advanced transferable skills across data science, statistics and machine learning. At the same time, the work is motivated by the real world challenge of improving weather forecast accuracy, which provides the opportunity to gain experience in working for and with the UK Met Office (UKMO).

The student will be based at the University of Exeter Streatham campus, but will also be expected to spend time at the UKMO in Exeter, collaborating with scientists there. The UKMO will provide CASE support so that the student will benefit from expertise in the weather forecasting team at the UKMO and training with respect to weather forecasting.

The student will also be expected to disseminate their work to the research community by attending relevant workshops and conferences across the UK, Europe but also internationally. There is a strong end-user element motivating this work, specifically the renewable energy industry, so the student will also be spending time with weather forecast users such as National Grid who strongly support this project.

The ideal candidate for this funded PhD scholarship should have a quantitative background and be interested in data analysis and fields such as statistics, data science and machine learning. They should also have an interest in computing, as it is envisioned that the techniques developed will be implemented on state-of-the-art cloud computing.

The scholarship includes UK tuition fees as well as £14,553 maintenance allowance per year. It also includes cover for development such as training courses and for travel (e.g. attending conferences).

Entry Requirements

You should have or expect to achieve at least a 2:1 Honours degree, or equivalent, in Mathematics or Statistics or Data Science. Experience in statistical modelling, machine learning, mathematical modelling 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.