PhD - Data-Driven Digital Twins for Measured Energy Systems

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. 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.

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. Link to apply: The National Physical Laboratory (NPL) is a world-leading centre of excellence that provides cutting-edge measurement science, engineering and technology to underpin prosperity and quality of life in the UK. Find out more about what it is like working here - NPL and DSIT have strong commitments to diversity and equality of opportunity, and welcome applications from candidates irrespective of their background, gender, race, sexual orientation, religion, or age, providing they meet the required criteria. Applications from women, disabled and black, Asian and minority ethnic candidates in particular are encouraged. All disabled candidates (as defined by the Equality Act 2010) who satisfy the minimum criteria for the role will be guaranteed an interview under the Disability Confident Scheme. At NPL, we believe our success is a result of the diversity and talent of our people. We strive to nurture and respect individuals to ensure everyone feels valued by treating everyone on the basis of their own individual merits and abilities regardless of their own or perceived identity, as part of our commitment to diversity & inclusion, we ensure we're creating an environment where all our colleagues feel supported and welcome. More about this on our page. We are committed to the health and well-being of our employees. Flexible working and social activities are embedded in our culture to create a positive work-life balance, along with a broad range of . are at the heart of what we do, and they shape the way we interact, develop our people and celebrate success. To ensure everyone has an equal chance, we're always willing to make reasonable adjustments to the recruitment process. If you would like to discuss, please