Opportunities

We are actively recruiting computational researchers to join the BASE Lab. If you are interested in autonomous experimentation, physics-informed machine learning, or synchrotron science, get in touch.

Open PhD Position: The Self-Driving Microscope

Predicting Stochastic Failure in Solid-State Batteries using Physics-Informed AI

4-year fully funded studentship · University of Greenwich · Ref: M34Impact-MSE2

This PhD is the foundational computational element of an 8-year project to build an autonomous, AI-piloted X-ray imaging platform that hunts for hidden failure points in solid-state batteries in real-time. As the founding PhD student of the BASE Lab, you will:

  • Build a multi-scale training dataset from 3D X-ray tomograms of solid-state cells (Li-metal/Li₆PS₅Cl) acquired at Diamond Light Source (I13-2), capturing pristine, aged, and post-failure stages.
  • Develop the core AI predictor using Graph Neural Networks (GNNs) to distinguish benign aging from true failure precursors — features retrospectively confirmed as the origin of a crack.
  • Integrate physics-based signatures by using OpenImpala to transform static 3D porosity maps into dynamic maps of local tortuosity and ionic flux, giving the AI physically grounded features to learn from.
  • Augment data with generative AI using physics-constrained diffusion models and GANs to create synthetic microstructures that improve model generalisation against noisy experimental data.

You will be co-supervised by Prof. Andrew Kao (validated simulation models) and Dr Mikhail Poluektov, and embedded in the M34Impact doctoral cohort. The lab has a computational core at Greenwich and an experimental hub at RAL, with direct access to Diamond Light Source and ISIS.

Funding

  • Bursary: £24,780/year (UKRI rate + £2,000 London weighting + £2,000 enhanced bursary), with annual uplifts
  • Fees: Covered at the University Home Rate (currently £5,006). International applicants may be fully covered for exceptional candidates.
  • Start date: September 2026

Requirements

  • 1st or Upper 2:1 in Computer Science, Physics, Materials Science, Engineering (or equivalent)
  • Strong Python programming skills
  • Motivation to learn machine learning and data science

Experience with deep learning, 3D image data, or HPC is desirable but not required.

How to Apply

Deadline: 17 April 2026

Apply online via the University of Greenwich application portal. Include reference number M34Impact-MSE2 in your personal statement, along with a CV (with 2 referees) and academic transcripts.

Contact james.lehoux@gre.ac.uk before applying to discuss the project.

General Enquiries

We welcome expressions of interest from prospective PhD students, postdocs, and visiting researchers at any time. If you have your own funding or want to discuss fellowship applications, please get in touch.

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