At a Glance
- Tasks: Develop a digital twin framework for energy systems using AI and physics-based models.
- Company: Join NPL and Mansim, leaders in measurement science and industrial applications.
- Benefits: Fully funded PhD with a tax-free stipend and tuition fees covered.
- Other info: Collaborative environment with access to industry case studies and expert training.
- Why this job: Make a real impact on low-carbon energy systems and advance your research career.
- Qualifications: Strong programming skills and a degree in engineering or related fields required.
The predicted salary is between 21805 - 21805 £ per year.
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 generalisable, 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 (e.g. 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. 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) with tuition fees paid; we expect the stipend to increase each year. The start date is October 2026.
Link to apply: https://www.findaphd.com/phds/project/data-driven-digital-twins-for-measured-energy-systems/?p184389
PhD - Data-Driven Digital Twins for Measured Energy Systems in Manchester employer: National Physical Laboratory
At NPL, we pride ourselves on being an exceptional employer, offering a collaborative work culture that fosters innovation and professional growth. Our PhD programme provides access to cutting-edge resources and expert mentorship, ensuring that you are well-equipped to tackle the challenges of modern low-carbon energy systems. With a focus on interdisciplinary collaboration and real-world applications, this role not only contributes to significant advancements in energy technology but also supports your personal and academic development in a supportive environment.
Contact Details:
National Physical Laboratory Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land PhD - Data-Driven Digital Twins for Measured Energy Systems in Manchester
✨Tip Number 1
Network like a pro! Reach out to professionals in the energy systems field, especially those working with digital twins or AI/ML. Attend relevant conferences or webinars and don’t be shy about introducing yourself – you never know who might have a lead on your dream PhD!
✨Tip Number 2
Show off your skills! Prepare a portfolio showcasing your programming projects, especially those involving Python, MATLAB, or any machine learning applications. This will give potential supervisors a taste of what you can bring to the table.
✨Tip Number 3
Practice makes perfect! Get ready for interviews by doing mock sessions with friends or mentors. Focus on explaining your research interests and how they align with the project’s goals, particularly around uncertainty quantification and Bayesian methods.
✨Tip Number 4
Apply through our website! It’s super easy and ensures your application gets the attention it deserves. Plus, we’re here to support you every step of the way, so don’t hesitate to reach out if you have questions!
We think you need these skills to ace PhD - Data-Driven Digital Twins for Measured Energy Systems in Manchester
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your application to highlight how your skills and experiences align with the PhD project. We want to see your passion for data-driven digital twins and how you can contribute to our goals.
Showcase Your Technical Skills:Don’t hold back on showcasing your programming skills and any relevant experience in machine learning or numerical modelling. We’re looking for candidates who can hit the ground running, so let us know what you’ve got!
Highlight Collaborative Experience:Since this project involves working across disciplines, it’s important to demonstrate your ability to collaborate with both academic and industrial teams. Share examples of teamwork that show you can thrive in a collaborative environment.
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It’s the best way to ensure your application gets the attention it deserves, so don’t miss out on this opportunity!
How to prepare for a job interview at National Physical Laboratory
✨Know Your Stuff
Make sure you brush up on your knowledge of energy systems, particularly photovoltaic arrays and battery storage. Familiarise yourself with the latest trends in digital twins and AI/ML frameworks. Being able to discuss these topics confidently will show that you're genuinely interested and well-prepared.
✨Show Off Your Skills
Highlight your programming skills, especially in Python, MATLAB, or C/C++. Be ready to discuss any projects you've worked on that involved machine learning, numerical modelling, or uncertainty quantification. Real examples will help demonstrate your capabilities and how they align with the role.
✨Ask Smart Questions
Prepare thoughtful questions about the project and the collaboration with NPL and Mansim. This shows that you're engaged and have done your homework. Ask about the datasets you'll be working with or how the team approaches uncertainty quantification in their models.
✨Be Ready to Collaborate
Since this role involves working across disciplines, be prepared to discuss your experience in teamwork and collaboration. Share examples of how you've successfully worked with others in academic or industrial settings, as this will highlight your ability to fit into their collaborative environment.
