Publisher
source

University College London

Fully Funded PhD in Reducing Uncertainty in the Impact of Climate on Biodiversity University College London in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Part of the UNRISK CDT offering 15-18 fully funded NERC studentships. Funding covers full university tuition fees, a personal stipend at standard UKRI rates, £6000 individual research and training costs, £5000 per student of cohort-level training, and a Flexible Fund for special projects. International applicants may need to cover visa costs and the international health surcharge (IHS).

Deadline

Jan 13, 2027

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Country

United Kingdom

University

University College London

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Keywords

Computer Science
Data Science
Paleontology
Environmental Science
Biology
Mathematics
Mathematical Modeling
Biodiversity
Marine Biology
Earth Science
Ecological Modeling
Statistics
Cambio Climático
ML

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About this position

PhD opportunity at University College London (Department of Earth Sciences) in reducing uncertainty in the impact of climate on biodiversity. This project sits at the intersection of climate science, biodiversity, ecological modelling, data science, machine learning, marine biology, marine sciences, palaeontology, and mathematical modelling.

The project addresses a key limitation in species distribution models (SDMs): they are often trained on relatively short ecological records and can be overfit to the current climatic window. By using palaeontology, historical ecology, museum collections, fossil archives, and other “dark data,” the PhD aims to build longer-term baselines and reduce uncertainty in predictions of how marine biodiversity responds to climate change.

The research will focus on two marine groups, arthropods and corals, and will develop AI-based methods such as text and image mining to extract ecological data from historical reports, collections, and fossil sources. The resulting datasets will span the Quaternary and will be used to compare models with and without long-term records, quantify uncertainty, and improve future projections for marine ecosystems and the services they provide.

The project is co-supervised by Dr Stephen Pates at UCL and Dr Lewis A. Jones at the Natural History Museum, London. The student will also have access to large collections of modern, historical, and fossil material.

Funding: This is a fully funded NERC studentship through the UNRISK CDT. It includes full tuition fees, a UKRI-rate stipend, research and training costs, cohort training support, and a flexible fund. International applicants are welcome, though awards are limited and visa/IHS costs are not covered.

Eligibility: Applicants should have a strong quantitative/computational background and be comfortable with R or Python. Experience in data analysis, statistics, or coding is highly desirable. A background in ecology, Earth science, biology, palaeontology, or computer science is suitable.

Application: Applications are open to UK and international applicants. The listing date shown is Wednesday, January 13, 2027. Check the UNRISK website and the FindAPhD listing for the latest application instructions and eligibility details.

Funding details

Part of the UNRISK CDT offering 15-18 fully funded NERC studentships. Funding covers full university tuition fees, a personal stipend at standard UKRI rates, £6000 individual research and training costs, £5000 per student of cohort-level training, and a Flexible Fund for special projects. International applicants may need to cover visa costs and the international health surcharge (IHS).

What's required

Applicants should have a strong quantitative and computational background in environmental/Earth science, ecology, biology, palaeontology, computer science, or a related discipline. They should be comfortable working with data in R or Python and have a genuine interest in applying data science and machine learning to ecological and palaeontological questions. Prior experience in data analysis, statistics, or coding is highly desirable. No prior fossil experience is required, but interest in deep-time perspectives and ecology is an advantage.

How to apply

Visit the UNRISK website for more information and apply through the PhD project listing. Check eligibility carefully, especially if applying from outside the UK, as international awards are limited by UKRI rules.

More information can be found here

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