Ferran Brosa Planella

Fully funded PhD opportunities are available at the University of Warwick through the HetSys Centre for Doctoral Training (CDT), focusing on energy storage research. The programme is designed for students from physical sciences, mathematics, and engineering who are passionate about applying mathematical and computational skills to solve complex, real-world problems. HetSys CDT provides interdisciplinary training in computational modelling for heterogeneous systems, preparing researchers to advance the field across diverse themes. Two highlighted projects for the next cohort include: Under Pressure: Predictive Modelling of Battery Ageing Across the Scales – This project addresses the challenges of lithium-ion battery ageing and gas generation, which can cause pressure build-up and cell failure. The research will develop physics-informed surrogate models using Bayesian machine learning to predict gas generation and pressure evolution, combining physical insight with data-driven approaches. The aim is to deliver fast, reliable predictions for safer, longer-lasting batteries. The project is co-supervised by Ferran Brosa Planella and Lukasz Figiel, in collaboration with JLR. Turning Up the Heat: Modelling and Scale-Up of Thermochemical Energy Storage – This project focuses on thermochemical energy storage (TCES) using NaOH-water systems, which offer a promising solution for storing heat through reversible chemical reactions. The research will develop hybrid modelling tools to improve design and scale-up, reduce costly experiments, and provide confidence intervals for better decision-making. The project is co-supervised by Ellen Luckins and Steven Metcalf. Applicants should have a strong background in physical sciences, mathematics, or engineering, and an interest in computational modelling, machine learning, or energy systems. The positions are fully funded, covering tuition fees and providing a stipend. The application deadline is January 28, 2026. For more information and to apply, visit the HetSys CDT website. Keywords: Energy Storage, Battery Ageing, Thermochemical Energy Storage, Bayesian Machine Learning, Physics-Informed Modelling, Computational Modelling, Lithium-ion Batteries, NaOH-water TCES, Physical Sciences, Mathematics, Engineering.