The Self-Driving Microscope: Predicting Stochastic Failure in Solid-State Batteries using Physics-Informed AI University of Greenwich in United Kingdom

The University of Greenwich invites applications for a fully funded PhD studentship as part of the pioneering 'Self-Driving Microscope' project, a major 8-year initiative aimed at revolutionising the detection and prediction of stochastic failure in solid-state batteries. This project addresses a critical national challenge: the unpredictable microscopic flaws that cause catastrophic battery failure, such as dendrite penetration and structural cracking, which are invisible to standard tests.

As the founding PhD student in the new BASE (Beamlines for Autonomous Science and Engineering) Laboratory, you will play a central role in designing and building the core predictive engine for an autonomous, AI-driven X-ray imaging platform. Your research will span the intersection of physics-informed artificial intelligence, materials science, and computational simulation. Key objectives include constructing a multi-scale training dataset using 3D X-ray tomograms of advanced solid-state cells, developing advanced AI models (such as Graph Neural Networks) to distinguish benign aging from true failure precursors, and integrating physics-based signatures using high-performance computing solvers like OpenImpala. You will also augment experimental data with generative AI models to improve robustness and generalisation.

The BASE Laboratory is structured with a computational core at the University of Greenwich and an experimental hub at the Rutherford Appleton Laboratory, offering direct access to national facilities and collaborative opportunities. The project is a cornerstone of the Computational Science and Engineering Group’s goals and is embedded within the M34Impact doctoral cohort. Supervision is provided by Dr James Le Houx (Faraday Institution Emerging Leader Fellow), Prof. Andrew Kao, and Dr. Mikhail Poluektov, ensuring expert guidance in simulation, AI, and battery imaging.

Funding is fully secured through the £9M Research England-funded M34Impact expansion programme. The studentship covers a generous stipend (Year 1: £24,780; subsequent years in line with UKRI rates plus London weighting and enhanced bursary) and a tuition fee contribution equivalent to the University Home Rate (£5,006/year). International applicants may need to pay the remainder tuition fee unless covered by M34Impact. The bursary is for 3 years with a possible extension of up to 12 months, subject to satisfactory progress.

Applicants should possess a strong academic background in physics, materials science, engineering, computer science, or mathematics, with experience in computational modelling, machine learning, or AI highly desirable. Programming skills and familiarity with high-performance computing are advantageous. International applicants must meet English language requirements and may be required to pay additional tuition fees.

The application deadline is April 17, 2026. To apply, submit your CV, academic transcripts, and a cover letter via the University of Greenwich portal, referencing M34Impact-MSE2. Informal enquiries to the supervisors are encouraged. This is a unique opportunity to join a dynamic research group and contribute to the next generation of battery technology and autonomous scientific platforms.