Supporting policymakers to incentivise effective and equitable land use change for Net Zero with AI UNRISK NERC Centre for Doctoral Training PhD studentship 2026/27 Entry. Ref: 5716

Supervisors

Primary Supervisor

Professor Daniel Williamson

Institution

University of Exeter (Land, Environment, Economics and Policy)

Academic Supervisors

Dr Amy Binner

Understanding Uncertainty to Reduce Climate Risks (UNRISK) is a Centre for Doctoral Training – Recruiting now!

UNRISK is a Centre for Doctoral Training with fully funded PhD research opportunities at the University of Leeds, University College London, and the University of Exeter collaborating with over 40 external partners. UNRISK will train students with the multidisciplinary knowledge and skills across climate science, data science and decision science to tackle the pressing challenge of reducing the risks associated with rapid climate change. UNRISK will fund 40 PhD students in cohorts of 12-15 per year over three years, providing a stipend, university fees and residential training for 3 years and 9 months. Find out more at https://unrisk-cdt.ac.uk/ and browse the projects at https://unrisk-cdt.ac.uk/projects/.

Project Information

Background

British law has enshrined specific goals to meet Net Zero, reverse biodiversity loss, improve water quality and to ensure food and energy security in the coming decades. To meet legally binding targets, we must use large amounts of UK land for tree-planting, agroforestry, re-wilding, peatland restoration, bioenergy crops and other green technologies. But, even if we knew where and when to deploy each change to the land, most land is privately owned, in large part by farmers. Policymakers must develop payment schemes that deliver the required land use change whilst providing value for money for the taxpayer. To support policymakers we aim to develop real-time uncertainty-enabled prediction of the response to changes in the land across all relevant ecosystem services and risks, scalable agent-based models of uptake of agri-environment schemes and the tools to explore and compare the efficacy of potential policies.

PhD Opportunity

Environmental impact models in our decision support tools include crop, livestock and whole-farm models, tree and soil-carbon simulators, water and nutrient flow models, abundance for 1100 species and an agroforestry model. Run time varies from seconds to 4 hours to simulate the impacts of a single land use change into the future on architectures on a spectrum from a humble laptop to the JASMIN supercomputer. We deploy deep Gaussian processes (DGP) to capture these models and their uncertainty within our decision support tools. GPs are a standard workhorse for uncertainty quantification in models, and are part of the core CDT training. However, both they and their deep cousins cannot fit in large dimensional input spaces (more than 12 is a struggle) because of the curse of dimensionality. Yet the impacts we are concerned with all require daily weather forcings to run. Our crop model, for example, requires 9 daily time-series inputs for simulations to take us out to Net Zero by 2050 (~82000 parameters).

Searching for low dimensional representations of parameter spaces so that effective machine learning models (like DGPs) are effective has been called “”active subspace”” selection, “”dimension reduction”” and “”embedding””. This PhD will study the problem from a new perspective. Inspired by the success of the transformer in large language models, we will explore tokenised weather series and develop transformers for dimension reduction in climate impacts models. We will tailor these to GPs and DGPs in the first instance and compare their performance to existing approaches in the UQ literature such as Automatic Relevance Determination and active subspaces. Improvements to our existing emulation will quickly be deployed into our decision support systems if we can establish trust in their uncertainty quantification. We will work with policymakers in establishing effective trust in the decision support tools generated with the technologies developed in this PhD.

Applicant Profile

Students with a strong background in machine learning, statistics or mathematics who want to learn about and work with novel techniques in AI but who want to make a difference in the environment or in UK policy at the same time will be well suited to this PhD.

Other information

https://netzeroplus.ac.uk/project/add-trees/
https://www.exeter.ac.uk/research/leep/researchimpact/

Funding

UNRISK will fund 40 PhD students in cohorts of 12-15 per year over three years, providing them tuition fees and a stipend for a period of 3 years 9 months which includes the expectation that PhD students will undertake a placement and also take part in cohort based residential research events. Further up to date information about studentship funding is available from UK Research and Innovation.

Applications are open to UK and international applicants, although the number of awards for international applicants is limited by UKRI rules. Please note that the grant cannot cover visa and NHS International Health Surcharge (IHS) costs, which are in the order of £3000-£4000 to allow overseas students entry to study in the UK.

Some additional places are also available for students who have their own funding, such as scholarships, and whose research is closely aligned with UNRISK.  

Part-time study can be offered to students unable to join the programme as a full-time student. Please get in touch with us if you would like to discuss this option. 

If you have any questions about the application process please email ENV-PGR@leeds.ac.uk.

Entry requirements

Academic Entry Requirements

You should hold a first or upper-second class Bachelor’s degree or a taught Master’s degree in an appropriate subject from a UK university. Non-UK qualifications of an equivalent standard are also accepted.

Residency

The UNRISK CDT studentships are available to UK and International applicants. Following Brexit, the UKRI now classifies EU students as international unless they have rights under the EU Settlement Scheme. The GW4 partners have agreed to cover the difference in costs between home and international tuition fees. This means that international candidates will not be expected to cover this cost and will be fully funded but need to be aware that they will be required to cover the cost of their student visa, healthcare surcharge and other costs of moving to the UK to do a PhD. All studentships will be competitively awarded and there is a limit to the number of International students that we can accept into our programme (up to 30% cap across our partners per annum).
 

English Language Requirements

If English is not your first language you will need to meet the English language requirements by the start of the PhD programme. This will be at least 6.5 in IELTS or an acceptable equivalent. Please refer to the relevant university for further information. Please refer to the English Language requirements web page for further information.

How to apply

You must apply for funding via the University of Leeds website further details can be found here Application Process – Understanding Uncertainty to Reduce Climate Risks

Please note that only those applications submitted directly via the University of Leeds application system will be assessed for funding. Applying directly to your chosen programme of study at the University of Exeter will not constitute an application for funding.

Data Protection

If you are applying for a place on a collaborative programme of doctoral training provided by University of Leeds and other universities, research organisations and/or partners please be aware that your personal data will be used and disclosed for the purposes set out below.

Your personal data will always be processed in accordance with the General Data Protection Regulations of 2018. University of Leeds (“University”) will remain a data controller for the personal data it holds, and other universities, research organisations and/or partners (“HEIs”) may also become data controllers for the relevant personal data they receive as a result of their participation in the collaborative programme of doctoral training (“Programme”).

Further Information

For an overview of the UNRISK NERC CDT programme please see the website https://unrisk-cdt.ac.uk/

Summary