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PhD Studentship: Multiscale Scientific Machine Learning Digital Twins for High-Torque Electric Propulsion under Extreme Duty Cycles University of Exeter in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
Available
Deadline
Dec 8, 2026
Country
United Kingdom
University
University of Exeter
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About this position
PhD Studentship at the University of Exeter in Multiscale Scientific Machine Learning Digital Twins for High-Torque Electric Propulsion under Extreme Duty Cycles.
This studentship sits at the intersection of electric propulsion, scientific machine learning, and digital-twin development for demanding heavy-duty applications such as maritime vessels, mining equipment, and off-highway machinery. The project addresses a key challenge for zero-emission propulsion: how to predict and manage high-torque, low-speed motor performance under severe operating profiles where electromagnetic, electromechanical, and thermal dynamics evolve on different timescales and are strongly coupled.
The research aims to build a multiscale SciML framework that can forecast thermal trajectories, torque capability, and efficiency variation in propulsion systems operating over long, variable duty cycles. The planned modelling approach combines deep neural networks for nonlinear electromagnetic behaviour and saturation effects, recurrent or neural state-space models for transient and load-history dependence, and neural ordinary differential equations for long-horizon thermal accumulation. The student will also work on physics-informed hybrid modelling, uncertainty quantification, and data-efficient experimental design so that reliable operating-envelope predictions can be learned from a limited number of carefully selected non-destructive tests.
Experimental work will be carried out at the University of Exeter’s Centre for Future Clean Mobility (CFCM), where the student will design safety-constrained dynamometer experiments and collect high-frequency electrical, mechanical, and thermal measurements to train and validate the digital twin. The project is collaborative with Ecomar Innovations, a marine power-systems company, and includes time on-site with the industrial partner to test the framework on real hardware and support improved thermal-management strategies.
This is a strongly interdisciplinary PhD with training in electric machines, SciML, thermal modelling, digital-twin validation, and industrial collaboration. The advertisement welcomes applicants from electrical/mechanical engineering, control systems, applied mathematics, or computer science.
Funding: The studentship provides funding of fees plus a stipend of £21,805 per annum for 2026/7 and is funded by the EPSRC Engineering and Physical Sciences Research Council.
Deadline: 2026-12-08.
Funding details
Available
What's required
Applicants from electrical engineering, mechanical engineering, control systems, applied mathematics, or computer science are welcome. The project is interdisciplinary and suited to candidates interested in electric machines, scientific machine learning, thermal modelling, digital twins, and experimental validation. No specific GPA or language-test requirements are stated in the posting.
How to apply
Apply through the University of Exeter funding application link. Review the project description and confirm your fit with the interdisciplinary technical background requested. Submit your application before 2026-12-08.
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Prepare your application
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Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.