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Dongfang Liang

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EPSRC FIBE3 CDT PhD Studentship with CamDragon: Data-Intensive AI Thermodynamic Models for Next-Generation Building Decarbonisation University of Cambridge in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Jun 30, 2026

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Country

United Kingdom

University

University of Cambridge

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Where to contact

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Keywords

Computer Science
Environmental Science
Mechanical Engineering
Heat Transfer
Civil Engineering
Python Programming
Energy Efficiency
Sensor Technology
Building Physics
Machine learning

About this position

[Fully-funded studentships covering fees and maintenance for eligible home students; limited funding for international students may be available later.]

The University of Cambridge is offering a fully-funded PhD studentship through the EPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment: Unlocking Net Zero (FIBE3 CDT), in partnership with CamDragon Co. Ltd. This four-year (1+3 MRes/PhD) programme focuses on developing data-intensive, AI-driven thermodynamic models to advance next-generation building decarbonisation. The research aims to optimise built-environment thermodynamics and occupant comfort by creating predictive AI tools for spatiotemporal heat transfer, identifying energy inefficiencies, and proposing adaptive strategies for carbon reduction.

CamDragon Co. Ltd, a Cambridge-based SME, brings expertise in engineering consultancy and STEM education, specialising in flood-risk evaluation, geohazard assessment, and sustainable drainage solutions across the UK, China, and Australia. The project objectives include constructing advanced AI algorithms for forecasting and visualising heat flow and occupant comfort metrics, identifying key drivers of energy inefficiency such as occupant behaviour and physical heat loss hotspots, and formulating AI-driven control strategies to optimise comfort and reduce carbon emissions. Additional goals involve assessing heat pump readiness and developing guidelines for scalable, data-rich design and operation frameworks in diverse building contexts.

Applicants should possess, or expect to obtain by the start date, a high 2.1 degree preferably at Masters level in Civil Engineering. Essential skills include data analytics, programming (Python, MATLAB), excellent communication, and the ability to integrate numerical modelling, sensor technologies, and occupant-focused design. Experience in thermodynamics, building physics, or machine learning is desirable, and familiarity with energy systems or HVAC design is advantageous. The studentship is fully funded (fees and maintenance) for eligible home students, with limited funding for international students considered later in the recruitment process. For project-specific enquiries, contact Prof. Dongfang Liang at [email protected]. For general enquiries, email [email protected].

Further information about eligibility and funding can be found at the provided links. The University of Cambridge actively supports equality, diversity, and inclusion, encouraging applications from all sections of society. Early applications are strongly recommended, as offers may be made before the stated deadline. To apply, use the online portal, state the course code EGEGR3 and project title, and note the £20 application fee.

Funding details

Available

What's required

Applicants must have, or expect to obtain by the start date, a high 2.1 degree preferably at Masters level in Civil Engineering. Required skills include data analytics, programming (Python, MATLAB), excellent communication, and the 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. Funding eligibility is primarily for home students, with limited international funding considered later.

How to apply

Apply online via the University of Cambridge postgraduate portal using the 'Apply' link. State course code EGEGR3 and project title in your application. There is a £20 application fee. Early applications are encouraged as offers may be made before the deadline.

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