Overview

Space2Health explores how satellite imagery can be transformed into meaningful measures of environmental exposures linked to health. The project relies on recent AI technologies to develop and evaluate satellite-derived indicators, examining how data choices and analytical approaches shape health-relevant evidence for research and decision-making.

About this opportunity

The global availability of high-resolution, high-frequency satellite imagery is transforming how environmental conditions are monitored and analysed at scale. Contemporary Earth Observation missions such as Sentinel-1 and Sentinel-2, Landsat-8/9 and commercial constellations deliver continuous, multi-sensor data streams describing urban form, green and blue infrastructure, air quality proxies, land surface temperature, land use dynamics and environmental change. In parallel, advances in geospatial artificial intelligence, including transformer architectures, foundation models, self-supervised learning and large-scale spatial embeddings, are redefining how complex spatial data can be processed and interpreted.

Despite these developments, a critical methodological gap remains. Many environmental health indicators rely on simplified or legacy remote sensing metrics that were not designed with health inference as their primary objective. Consequently, the analytical potential of modern satellite systems is not fully realised in public health research. Robust, interpretable and transferable approaches are needed to translate raw satellite data into meaningful, health-relevant spatial evidence.

This PhD will address that challenge by developing and evaluating next-generation satellite-derived indicators for health applications. The emphasis will be on methodological innovation: designing indicators that are explicitly fit for purpose for robust, scalable and interpretable spatial inference in health research. Rather than relying on conventional indices, the project will examine how design decisions influence the representation of environmental conditions and their linkage to health outcomes.

The candidate will investigate how modelling choices, including spatial resolution, temporal aggregation, feature engineering, multi-sensor data fusion (optical, SAR, thermal), and machine learning architectures, shape how environmental signals are extracted, structured and connected to health-relevant processes. Approaches may include convolutional neural networks, transformer-based models, graph neural networks, embedding-based representations of spatial structure and uncertainty-aware spatial statistical frameworks. Particular attention will be given to interpretability, reproducibility and uncertainty quantification, ensuring outputs are scientifically rigorous and policy-relevant.

Through empirical case studies, the project will examine how alternative methodological pipelines shape health-related inference, including analyses of environment–health relationships, spatial vulnerability and inequalities in environmental conditions. In doing so, it will develop a transferable framework for designing and evaluating satellite-based health indicators across spatial contexts. By bridging satellite science, geospatial AI and public health, the research will advance spatial health analytics and support equitable, data-driven decision-making.

Training, Collaboration and Project Structure

The candidate will gain advanced expertise in satellite data processing, geospatial AI, spatial epidemiology and uncertainty quantification. Training will include work with large-scale Earth Observation platforms (e.g. Google Earth Engine) and Python-based deep learning frameworks (e.g. PyTorch, TensorFlow), providing highly transferable technical skills.

Year one will focus on methodological design, advanced training and dataset development, followed by model development, empirical evaluation and journal outputs in years two and three. The final phase will synthesise findings into a coherent methodological framework with clear policy relevance. The project offers an opportunity to work at the forefront of satellite-enabled health research, equipping the candidate with interdisciplinary expertise increasingly in demand across academia, government and the geospatial sector.

Who is this for?

This project is suitable for candidates with a strong academic background in geography, environmental science, remote sensing, data science, computer science, public health, or a related discipline, typically holding a UK First Class or Upper Second Class degree (or equivalent). Applicants should demonstrate strong analytical skills and an interest in applying geospatial and data-driven methods to health and environmental applications; prior experience with programming or geospatial data is desirable but not essential.

How to apply

1. Contact supervisors

Candidates wishing to apply should complete the University of Liverpool application form to apply for a PhD in Geography

Please review our guide on How to apply for a PhD | Postgraduate research | University of Liverpool carefully and complete the online postgraduate research application form to apply for this PhD project.

Please ensure you include the project title and reference number SOES003 when applying.

Supervisors	Email address	Staff profile URL
Ron Mahabir	Ron.Mahabir@liverpool.ac.uk	https://www.liverpool.ac.uk/people/ron-mahabir
Prof. Dani Arribas-Bel	darribas@liverpool.ac.uk	https://www.liverpool.ac.uk/people/daniel-arribas-bel

2. Prepare your application documents

You may need the following documents to complete your online application:

A research proposal (this should cover the research you’d like to undertake)
University transcripts and degree certificates to date
Passport details (international applicants only)
English language certificates (international applicants only)
A personal statement
A curriculum vitae (CV)
Contact details for two proposed supervisors
Names and contact details of two referees.

3. Apply

Finally, register and apply online. You'll receive an email acknowledgment once you've submitted your application. We'll be in touch with further details about what happens next.

Funding your PhD

This UoL funded Studentship will cover full tuition fees (for 2025-26 this is £5,006 pa.) and pay a maintenance grant for 3.5 years, at the UKRI standard rates (for 2025-26 this is £20,780 pa.) The Studentship also comes with access to additional funding in the form of a Research Training Support Grant to fund consumables, conference attendance, etc.

UKRI Studentships are available to any prospective student wishing to apply including both home and international students. While UKRI funding will not cover international fees, a limited number of scholarships to meet the fee difference will be available to support outstanding international students.

We want all of our Staff and Students to feel that Liverpool is an inclusive and welcoming environment that actively celebrates and encourages diversity. We are committed to working with students to make all reasonable project adaptations including supporting those with caring responsibilities, disabilities or other personal circumstances. For example, If you have a disability you may be entitled to a Disabled Students Allowance on top of your studentship to help cover the costs of any additional support that a person studying for a doctorate might need as a result. We believe everyone deserves an excellent education and encourage students from all backgrounds and personal circumstances to apply.

Careers

PhD: Space2Health: Turning Pixels into Health Evidence

Location: University of Liverpool

Salary: £20,780 pa

Hours: Full time

Contract: 3.5 years

Posted on: 5 March 2026

Closing date: 13 April 2026