[School of Engineering PhD Scholarships] Transferable Digital Twins for Smart and Social Homes: Explainable AI for Inclusive, Flexible and Low-Carbon Energy

About the project

About the Project Homes are becoming smarter through technologies such as solar panels, batteries, heat pumps, EV chargers and smart appliances. However, their real-world benefits depend on how technologies, buildings and people interact. This PhD will develop transferable digital twins for smart and social homes, using Explainable Artificial Intelligence (XAI) to predict energy use and explain why homes perform differently. It will address limitations of current approaches, including fragmented data and difficult-to-explain “black-box” AI models. A major strength is access to a UK Living Lab of 50+ homes, supported by Entrust Microgrid Ltd and GMCA, spanning privately owned and social/council housing. Using the EnSmartHEMS platform, the student will analyse real-world data on household electricity, low-carbon technologies, environmental conditions and energy-related behaviour. The research will combine digital twins, machine learning, Explainable AI and scenario modelling to understand what drives household energy performance and test whether solutions can transfer across different homes and household contexts. The student will gain advanced technical skills alongside valuable experience working with industry and public-sector partners. The ultimate goal is practical, that is, smarter and more inclusive homes that reduce energy costs and carbon emissions while maintaining comfort and supporting a flexible, low-carbon electricity system. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships . If you don’t already have an applicant account, please follow the instructions here. . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility : The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in a discipline directly relevant to the PhD such as engineering, built environment, energy, computer/data science(or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in a discipline directly relevant to the PhD such as engineering, built environment, energy, computer/data science (or international equivalent). Previous research experience in Python, machine learning, energy modelling or data analysis is desirable. This project will remain open until filled. If your application is submitted by 1 st November 2026, you can expect a decision by 18 th December 2026. If your application is submitted by 15 th January 2027, you can expect a decision by 30 th March 2027. Self or externally funded students can also be considered for this project. FSESoE

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