PhD Studentship: ML-Accelerated Excited-State Simulations of Perovskite Optoelectronics

Halide perovskites are a fascinating class of materials with a wide range of applications in (spin)optoelectronics. Their outstanding optoelectronic properties are strongly influenced by coupling between excited electronic states and the wildly vibrating atoms in these “soft semiconductors”. This can lead to the trapping of charge carriers and excitons (bound electron-hole pairs), shifted and broadened spectral signatures, and – paradoxically – either constitute a desirable material property or limit device performance. Understanding and predicting these excited-state phenomena “from first principles” is essential for rational materials design, but the underlying numerical simulations are computationally expensive, limiting studies to small systems and short timescales.

This project will explore how machine-learning-based methods can help overcome this bottleneck, opening the door to excited-state simulations at scales and system sizes that are currently out of reach. You will work at the interface of method development and materials physics, as part of a research group passionate about tackling complex materials challenges for materials with real-world applications.

The project offers training in first-principles electronic-structure and excited-state methods (density functional theory and the GW and Bethe-Salpeter-Equation approaches), machine learning for atomistic simulation, and high-performance computing, within an active, collaborative research group.

You will be based in the group of Prof. Linn Leppert in the School of Metallurgy and Materials at the University of Birmingham.

Background:

We welcome applicants from Physics, Materials Science, Chemistry, or related disciplines who have at least an Upper Second-Class Honours Degree or equivalent. A background including courses in quantum mechanics or quantum chemistry is required; prior experience with electronic-structure theory or numerical simulation methods is an advantage but not essential, as full training will be provided.

How to apply:

Further details about the research group can be found at www.leppertlab.com. If you are interested in this position, please contact me (l.leppert@bham.ac.uk) including a CV and the names and contact details of two references.

Do not apply through the University of Birmingham's admission system until instructed to.

Closing date: 31 August 2026. Early applications are encouraged, as the position may close once a suitable candidate has been found.

Funding notes:

This position is a fully funded 3.5-year PhD studentship (UK home fees only).

Advert information