EPSRC FIBE3 CDT PhD studentship with CamDragon: Data-Intensive AI Thermodynamic Models for Next-Generation Building Decarbonisation Full-time

Cambridge EPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment

This is a four-year (1+3 MRes/PhD) studentship funded through the Cambridge EPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment: Unlocking Net Zero (FIBE3 CDT).

The project is funded in collaboration with CamDragon Co. Ltd, a Cambridge-based SME offering engineering consultancy and STEM education and specialising in flood-risk evaluation, geohazard assessment, and sustainable drainage solutions across the UK, China, and Australia.

This research develops a data-intensive, AI-driven framework to optimise built-environment thermodynamics and occupant comfort by creating predictive AI tools for spatiotemporal heat transfer. Machine learning algorithms will identify energy inefficiencies and propose adaptive strategies while combined theoretical and empirical approaches enhance wellbeing and reduce carbon emissions.

Project objectives:

1. Construct AI algorithms employing advanced thermodynamic and machine learning models to forecast and visualize heat flow and occupant comfort metrics.
2. Identify key drivers of energy inefficiency, including occupant behaviour patterns and physical heat loss hotspots.
3. Formulate and validate AI-driven control strategies that optimise comfort and carbon reduction.
4. Assess heat pump readiness and propose targeted interventions to facilitate low-carbon retrofits.
5. Produce guidelines for scalable, data-rich design and operation frameworks within diverse building contexts.

Applicants should have (or expect to obtain by the start date) a high 2.1 degree preferably at Masters level in Civil Engineering, skills in data analytics and programming skills (Python, MATLAB), excellent communication and ability to integrate numerical modelling, sensor technologies, and occupant-focused design. Experience in thermodynamics, building physics, or machine learning is desirable. Familiarity with energy systems or HVAC design is advantageous.

Fully-funded studentships (fees and maintenance) are only available for eligible home students in the first instance. A limited number of international students can be considered for funding at a later stage in the recruitment process. Further details about eligibility and funding can be found at:

Applications should be made online via the University of Cambridge Applicant Portal stating course code EGEGR3 with Project: Data-Intensive AI Thermodynamic Models for Next-Generation Building Decarbonisation with Prof Dongfang Liang. Please note that there is a £20 application fee.

Early applications are strongly encouraged as an offer may be made before the stated deadline.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.