Climate Change Scholarship Opportunities in UK for Pakistani Students

Pakistani students can apply for two fully-funded PhD studentships at University College London (UCL) under the NERC-funded UNRISK Centre for Doctoral Training for 2027 entry.

The opportunities cover research in climate change, biodiversity, statistics, and machine learning. Successful candidates will receive full tuition coverage, a UKRI maintenance stipend, and dedicated research and training support.

The two UCL projects are Reducing Uncertainty in the Impact of Climate on Biodiversity and Robust and Scalable Spatio-Temporal Modelling.

Applications for the 2027 intake will open on 16 November 2026 and close on 13 January 2027 at 1:00 PM GMT.

UCL Climate PhD Studentships

1. Reducing Uncertainty in the Impact of Climate on Biodiversity

The first PhD project focuses on improving predictions about how climate change affects biodiversity.

Scientists commonly use Species Distribution Models (SDMs) to estimate how species may respond to changing temperatures and other climatic conditions. However, many of these models rely on ecological monitoring records covering only a relatively short period.

The UCL project aims to improve these predictions by incorporating long-term information from fossil records, museum collections, historical surveys, and other palaeontological sources.

The research will focus particularly on marine biodiversity, including arthropods and corals.

The selected researcher will use computational approaches, including text and image mining, to extract ecological information from historical and palaeontological sources.

These datasets will help researchers build a longer-term picture of how species have responded to environmental change and assess how this additional information affects climate-related biodiversity predictions.

The project will combine climate science, biodiversity research, ecological modelling, palaeontology, and data science.

Who is Suitable for the Biodiversity Project

Applicants with backgrounds in environmental science, Earth sciences, ecology, biology, palaeontology, computer science, or related disciplines may be suitable. Strong quantitative and computational skills will be useful.

Candidates with experience using R or Python, statistics, programming, data analysis, or machine-learning techniques may be particularly well suited to the project. Previous experience working with fossils is not essential.

2. Robust and Scalable Spatio-Temporal Modelling

The second PhD studentship focuses on developing better statistical and machine-learning methods for climate prediction.

Climate scientists increasingly use large datasets and probabilistic models to predict environmental changes and estimate uncertainty.

However, climate observations can be incomplete, irregularly distributed, or affected by measurement errors. These problems can reduce the reliability of predictions.

The project will develop mathematical and computational approaches that remain reliable even when observations or modelling assumptions are imperfect.

Research areas will include Gaussian processes, Kalman filtering, generalised Bayesian inference, robust statistics, and scalable machine-learning methods.

The student will examine how different techniques affect prediction accuracy, uncertainty estimates, and computational requirements.

A key part of the research will involve climate datasets, including satellite observations of the cryosphere and polar regions.

The project will combine mathematical theory with practical climate applications and large-scale computational analysis.

Who is Suitable for the Spatio-Temporal Modelling Project?

Candidates with a strong background in mathematics, statistics, computer science, machine learning, or another quantitative discipline may be suitable.

Applicants should have good programming skills and an interest in mathematical and computational research.

Previous research experience in statistics or machine learning can strengthen an application but is not necessarily required.

Applicants with relevant professional or industry experience may also be considered.

Previous specialist training in climate science is not essential, but candidates should demonstrate an interest in climate and environmental applications.

What Does the Funding Cover

UNRISK provides comprehensive financial support to successful PhD candidates. The studentship covers full university tuition fees and provides a maintenance stipend at the standard UKRI rate for 3 years and 9 months.

The current London-weighted UKRI stipend for 2026-27 is £23,805 per year. The stipend payable from the 2027 academic year will follow the applicable UKRI rate at that time.

Each student also receives a £6,000 individual Research Training and Support Grant. In addition, the program provides approximately £5,000 worth of cohort-level training per student.

The funding period also includes an expected three-month placement, giving researchers experience beyond their core academic work.

Funding for Pakistani Students

Pakistani students are eligible to apply as international candidates. Successful international students who receive an overseas-funded place can receive coverage for their international tuition fees alongside the maintenance stipend and research support.

However, the number of awards available to international students is limited under UKRI funding rules.

Pakistani candidates will therefore compete for a restricted number of international-funded places.

Applicants should also note that the studentship does not cover UK visa fees, relocation expenses, or the Immigration Health Surcharge.

Academic Eligibility

Applicants normally need at least a UK 2:1 honours degree or an equivalent international qualification in a subject relevant to their selected project.

Relevant Master’s-level study can also be considered during the selection process. UNRISK does not state a general GPA 3.3 requirement for these studentships.

Candidates who already hold a PhD or are currently registered for a PhD are not eligible to apply.

English Language Requirements

International students whose first language is not English must meet the English-language requirements of the university hosting their selected research project.

Applicants choosing either of these two projects will need to meet UCL’s applicable postgraduate research English requirements before enrolment.

Candidates do not necessarily need to have completed their English-language test by the initial UNRISK application deadline.

How to Apply

UNRISK uses a central application process administered through the University of Leeds, even when the selected project is based at UCL.

Applicants must complete the required University of Leeds application and the separate UNRISK application form.

Candidates should select NERC UNRISK CDT as their intended course of study. Applicants can identify up to two PhD projects in their application.

UNRISK also encourages candidates to contact the relevant project supervisors before applying. This can help applicants understand the research requirements and determine whether their academic background matches the project.

Find out more details about the  Robust and Scalable Spatio-Temporal Modelling PhD Studentship and 

Reducing Uncertainty in the Impact of Climate on Biodiversity PhD Studentship

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