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Fully-funded PhD position: AI-Accelerated Discovery of Energy Materials

We’re looking for a PhD student at the University of Cambridge to help accelerate the discovery of materials for grid-scale energy storage – an important part of the global transition to renewable energy.

This interdisciplinary, four-year studentship (starting October 2025) will combine advanced machine learning methods with novel experimental datasets at the Cavendish Laboratory. The student will develop new computational approaches to identify and understand factors driving battery degradation, efficiency, and lifespan.

The project is jointly supervised by Akshay Rao (Physics), James Fergusson and myself (DAMTP), with additional collaboration from Chemistry, the spinout company illumion , and the broader AI-for-Science community in Cambridge. Students with strong backgrounds in physics, chemistry, materials science, applied mathematics, or related disciplines are particularly encouraged to apply.

Deadline: 10 May, 2025
Full details in the flyer attached.

If you know students who might be interested, please do share or circulate within your networks. Thank you!

Funding details

This is a fully-funded, four-year PhD studentship. Funding covers tuition and stipend. No specific stipend amount is mentioned.

What's required

Applicants should have a strong background in physics, chemistry, materials science, applied mathematics, or related disciplines. No specific GPA, language test, or degree level requirements are mentioned, but a relevant undergraduate or master's degree is implied. Interdisciplinary skills and interest in machine learning and experimental datasets are preferred.

How to apply

Review the flyer for full details. Prepare your application materials and submit them to the University of Cambridge by the stated deadline. Contact the supervisors if you have questions. Share or circulate the opportunity within your networks.

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