Data-Driven Digital Twins for Measured Energy Systems

About the Project

Project overview:

Modern low-carbon energy systems such as photovoltaic (PV) arrays and battery energy storage systems (BESS) generate extensive measurement data (electrical, thermal, imaging and diagnostic). However, there is currently no generic, metrology-grounded AI/ML framework that fuses these heterogeneous data with physics-based models to create trustworthy, asset-specific digital twins with quantified uncertainty.

This project will develop a measurement-science-driven digital twin framework for energy assets, initially demonstrated on PV modules/fields and battery systems using existing NPL datasets. The work will integrate suitable physics-based models (for example PV performance modelling, electro-thermal and thermofluid dynamics) with deep learning and multi-fidelity modelling. Bayesian fusion/inference methods will also be integrated for state estimation, uncertainty quantification, anomaly detection, remaining-life prediction and operational optimisation.

Training environment and collaboration

• Mansim (www.mansim.com) will provide industrial supervision, training and access to commercial CFD/AI platforms and representative industrial case studies, supporting rapid translation of outcomes into practice.
• NPL (www.npl.co.uk) will provide the measurement-science foundation, calibrated datasets, specialist support in data science and uncertainty, and host the student for an extended placement with facilities and training.

Research aims and indicative work packages:

• Develop a generalizable, multisensory digital twin methodology for PV and battery systems that is metrology-guided and uncertainty-aware.
• Create Bayesian data fusion and uncertainty quantification approaches that deliver traceable confidence intervals for model outputs to aid decision making.
• Validate the framework using calibrated datasets (including ageing, diagnostic, thermal and electrical performance measurements).
• Demonstrate asset health assessment capabilities including anomaly detection and remaining-life prediction with quantified uncertainty.
• Align outputs with emerging best practice in digital metrology for energy systems and support dissemination through stakeholder engagement routes.

Training environment and collaboration:

NPL will provide the measurement-science foundation, calibrated datasets, specialist support in data science and uncertainty, and host the student for an extended placement with facilities and training.

Mansim will provide industrial supervision, training and access to commercial CFD/AI platforms and representative industrial case studies, supporting rapid translation of outcomes into practice.

Eligibility

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

Essential:

• Degree in engineering, physical sciences, computer science, or a closely related discipline (typically first-class or high 2:1, or equivalent; Master’s welcome)
• Strong programming skills (for example Python, MATLAB, C/C++)
• Strength in at least two of: machine/deep learning, numerical modelling, statistics, optimisation, scientific computing
• Ability to work across disciplines and collaborate with academic and industrial teams

Desirable:

• Experience in Bayesian inference, probabilistic modelling, or uncertainty quantification
• Experience in deep learning for time-series, imagery, and/or multimodal data
• Energy systems knowledge (PV, batteries) or experience with real measurement datasets
• Physics-based simulation, surrogate modelling, or multi-fidelity methods

Funding

This 3.5-year PhD project is fully funded and home students, and EU students with settled status, are eligible to apply. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£21,805 for 2026/27) and tuition fees will be paid. We expect the stipend to increase each year. The start date is October 2026.

We recommend that you apply early as the advert may be removed before the deadline.

Before you apply

We strongly recommend that you contact the academic supervisors (Prof Hujun Yin - Hujun.yin@manchester.ac.uk and Dr Amir Keshmiri - a.keshmiri@manchester.ac.uk) for this project before you apply. Please send your CV along with a cover letter about your motivation to study this PhD project.

How to apply

Apply online through our website: https://uom.link/pgr-apply-2425

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

After you have applied you will be asked to upload the following supporting documents:

• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
• Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
• English Language certificate (if applicable)

If you have any questions about making an application, please contact our admissions team by emailing FSE.doctoralacademy.admissions@manchester.ac.uk.