Multi-scale Computational & AI-Enabled Modelling of Electrochemical Energy Technologies (Ref: AAE-ZN-2514)

About the Project

The rapid development of advanced electrochemical technologies, including batteries, fuel cells, electrolysers, is driving demand for high-performance, cost-effective, and durable systems. Traditional experimental approaches alone are insufficient to fully capture the complex interactions across multiple length and time scales that govern electrochemical behaviour.

This PhD project aims to combine multi-scale computational modelling (from atomistic to device levels) with artificial intelligence (AI) to develop digital twins of electrochemical systems. These tools will accelerate material discovery, optimise electrode/electrolyte design, and enable predictive lifetime modelling and system reliability assessment.

The successful candidate will join the School of Aeronautical, Automotive, Chemical and Materials Engineering at Loughborough University and work in an interdisciplinary environment at the interface of materials science, electrochemistry, modelling, and data science.

Name of primary supervisor/CDT lead:
Dr Zhiqiang Niu Z.Niu@lboro.ac.uk

Name of secondary supervisor:
Professor Valerie Pinfield
https://www.lboro.ac.uk/departments/chemical/staff/valerie-pinfield/

Entry requirements:
A minimum of an upper-class honours degree (2:1) or overseas equivalent in a STEM subject area, or equivalent relevant industrial experience. Working knowledge of machine learning is desirable but not mandatory.

English language requirements:
Applicants must meet the minimum English language requirements. Further details are available on the International website (http://www.lboro.ac.uk/international/applicants/english/).

Bench fees required: No

Closing date of advert: 1st July 2026

Start date: July 2026

Full-time/part-time availability: Full-time 3.5 years

Fee band: 2025/26 Band RB (UK £5,006, International £28,600)