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Award details

UKRI iCASE IBM Studentship - Bayesian Methods For Climate Impact Uncertainty Quantification. PhD in Mathematics Ref: 4386

About the award


Dr Stefan Siegert, College of Engineering, Mathematics and Physical Sciences, Streatham Campus, University of Exeter


Department of Mathematics, Streatham Campus, Devon, University of Exeter.

The University of Exeter’s College of Engineering, Mathematics and Physical Sciences  is inviting applications for a fully-funded PhD studentship. The studentship will cover Home tuition fees plus an annual tax-free stipend of at least £15,609 for 3.5 years full-time, or pro rata for part-time study.

This College studentship is open to UK and Irish nationals, who if successful in their application will receive a full studentship including payment of university tuition fees at the home fees rate.

Project Description:

The goal of this project is to develop novel statistical methodology to reliably quantify risk of high impact climate events, using all available sources of information, such as simulation models, physical constraints, satellite observations and station data. Robust and reliable methods that can combine physical models and observation will allow industry and society build resilience to adverse effects of climate change.

A key gap in the research to date is appropriate methodology to fully address heterogeneities across the available simulation models and observation data in resolution, domain, availability, and most significantly, uncertainty. Failure to correctly combine and propagate uncertainties from diverse data sources can result in miscalibrated, often over-confident, predictions which ultimately lead to poor decision making. 

This project will develop Bayesian hierarchical models to combine various observational and simulation data to infer future climate, taking into account observational uncertainties, heterogeneities between climate models, and discrepancies between climate models and real-world climate due to unresolved physical processes. The methodology will leverage and combine the strengths of the various data streams.

Challenges in this project include the processing and combination of massive climate data sets, at different aggregation levels, different patterns of missingness, and diverse sources of biases and errors. Statistical inference in large spatio-temporal data requires advanced computational and mathematical methods, such as distributed computing, sparse linear algebra, and numerical approximations for Bayesian inference.

The successful applicant will have access to IBM’s geospatial data, analytics and cloud computing facilities. They will study existing statistical modelling approaches to infer future climate from observation and climate model data. The main goal of the project is to develop new methodology that can handle huge amounts of data to infer probabilities of high-impact climate events such as floods, droughts, or heatwaves, in a mathematically coherent way. The developed methods will be applied to downscale, regionalise and aggregate predictions to relevant spatial and temporal scales. A further extension of the methodology will consist in reliably estimating compound risks, such as the co-occurrence of heat waves and floods. Ultimately, the methodology will be validated with focus on commercial applications relevant to IBM such as supply chain management, financial services, utilities, and infrastructure planning.

The studentship is funded via the EPSRC Industrial Cooperative Awards in Science & Technology (CASE) scheme, via a grant awarded to IBM.  The successful applicant will be required to undertake regular reporting and visits to the sponsor.  For more details about the Industrial CASE scheme, see 

The successful applicant will be aligned to the UKRI CDT in Environmental Intelligence, and will be included in CDT cohort building and training activities where suitable.  Unlike standard CDT students, the successful applicant will be attached to the above project from the beginning of their PhD under the supervision of Stefan Siegert, and will not be permitted to change to a different project.

About the UKRI Centre for Doctoral Training in Environmental Intelligence

Our changing environment presents a series of inter-related challenges that will affect everyone’s future health, safety and prosperity. Environmental Intelligence (EI) is the integration of environmental and sustainability research with data science, artificial intelligence and cutting-edge digital technologies to provide the meaningful insight to address these challenges and mitigate the effects of environmental change.  One of the 16 UKRI AI CDTs launched in 2019, the CDT in Environmental Intelligence provides an interdisciplinary training programme for students covering the range of skills required to become a leader in EI:

  • the computational skills required to analyse data from a wide variety of sources;
  • expertise in environmental challenges;
  • an understanding of the governance, ethics and the potential societal impacts of collecting, mining, sharing and interpreting data, together with the ability to communicate and engage with a diverse range of stakeholders.

The CDT cohort (currently around 20 students out of the planned 50), works and learns together, bringing knowledge, skills, and interests from a range of academic disciplines relevant to EI.  CDT students undertake training and professional development as a cohort, and regularly participate in seminars, symposia, and partner engagement activities including the annual CDT Environmental Intelligence Grand Challenge.  As part of the research community at the University of Exeter, CDT students benefit from networking with colleagues in the Institute for Data Science and Artificial Intelligence; the Global Systems Institute; and the Environment and Sustainability Institute.

Entry requirements

This studentship is open to UK and Irish nationals, who if successful in their application will receive a full studentship including payment of university tuition fees at the home fees rate.

Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of mathematics, statistics, science or technology and ideally a postgraduate degree in a suitable subject area.

If English is not your first language you will need to have achieved at least 6.0 in IELTS and no less than 6.0 in any section by the start of the project.

Alternative tests may be acceptable (see

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.
  • Research proposal
  • 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, please see the entry requirements for details.
  • Two references
  • Reference information

It is your responsibility to ensure that your referees email their references to, as we will not make requests for references directly.  Please note that applications with missing documentation will not progress to shortlisting.

References should be 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.

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


Application deadline:20th December 2021
Number of awards:1
Value:Home tuition fees plus an annual tax-free stipend of at least £15,609 for 3.5 years full-time
Duration of award:per year
Contact: PGR Admissions Team