Sun in, heat out: multi-sensor modelling of building solar gains and thermal losses for energy-efficient retrofit targeting

Achieving large-scale energy retrofit of existing buildings is critical to meeting UK and European net-zero targets, yet progress remains slow due to high audit costs and limited availability of reliable, building-level performance data. Much of the future building stock already exists, and many buildings—particularly those constructed before the 1990s—suffer from poor insulation, high heat losses, and vulnerability to energy poverty. Current assessment approaches are often fragmented, treating solar gains and thermal losses separately and relying on labour-intensive surveys that do not scale to city level.

This PhD will develop a novel, integrated framework to quantify both sides of the building energy balance—sun in (solar gains) and heat out (thermal losses)—using multi-sensor Earth Observation (EO) data, in-situ measurements, and machine learning. The research will combine high-resolution thermal infrared satellite data, drone-based thermography, LiDAR-derived 3D building models, multispectral imagery, and indoor measurements to diagnose building performance at façade and neighbourhood scales.

Two comparative urban case studies will be developed: one in the UK, in collaboration with local authorities and industry partners, and one in Romania, enabling cross-climate and cross-construction analysis. The project will integrate physics-informed and data-driven models to distinguish fabric-related heat losses from behavioural effects and urban microclimate influences.

The main outcome will be a transferable, scalable decision-support framework that enables local authorities and planners to prioritise retrofit interventions more effectively, directing investment where it can deliver the greatest energy and carbon savings. The project will provide the student with advanced training in EO analytics, AI, urban energy modelling, and interdisciplinary research, while contributing directly to national and international net-zero strategies.

Applicants should have (or expect to obtain) a good undergraduate degree (minimum 2:1 or equivalent) and/or a Master’s degree in a relevant discipline such as engineering, environmental science, geography, geospatial science, remote sensing, data science, or a related field.

The ideal candidate will have an interest in one or more of the following areas: building energy performance, urban sustainability, Earth Observation, GIS, remote sensing, or data-driven environmental modelling. Experience with quantitative data analysis is desirable, including working with spatial datasets, programming or scripting (e.g. Python, R, MATLAB), and applying statistical or machine-learning methods.

An interest in interdisciplinary research, combining engineering, environmental science, and data analytics, is essential.

The successful candidate should be motivated, well organised, and able to work independently as well as collaboratively with academic and external partners. Good written and verbal communication skills in English are required.

Open to any UK or international candidates. Up to 30% of our UKRI funded studentships can be awarded to candidates paying international rate fees. Find out more about eligibility.