Dynamic Behaviour and Engineering Optimisation of PEM Water Electrolysers under Real-World Conditions with AI Support Monash University Malaysia in Malaysia

Green hydrogen is a pivotal element in the global transition to net-zero carbon emissions by 2050, offering clean energy potential. The widespread adoption of green hydrogen depends on the engineering of reliable, efficient, and durable water electrolysers, which convert water into hydrogen through electrochemical splitting. However, electrolyser systems face challenges due to dynamic operation, fluctuating renewable energy inputs, complex physico-chemical processes, and gradual degradation, all of which impact efficiency, stability, and cost-effectiveness.

This PhD project at Monash University Malaysia focuses on the design, operation, and optimisation of proton exchange membrane (PEM) water electrolysers. The research emphasises understanding both transient and steady-state behaviours under realistic operating conditions. Key areas include detailed characterisation of system responses under variable load profiles, engineering strategies to mitigate degradation and extend component lifetimes, optimisation of single- and multi-stack configurations for scalability and efficiency, and integration of physio-chemical and electrochemical insights into advanced design frameworks.

Artificial intelligence (AI) and machine learning (ML) will be leveraged as enabling tools to accelerate discovery, prediction, and optimisation. Data-driven models will be used for predictive failure analysis, real-time performance monitoring, and proactive maintenance planning. The combination of experimental engineering and AI-driven modelling aims to deliver robust predictive tools and design guidelines that enhance stability, reduce costs, and enable the next generation of PEM electrolysers.

Ultimately, this research will contribute to lowering the levelised cost of green hydrogen and support its integration into sustainable energy systems worldwide. The project is funded by Monash University Malaysia, offering a stipend and fully-funded tuition fees to successful PhD candidates.

Applicants should have a first class degree in Engineering or Science (preferably Chemical Engineering, Physics, or Chemistry), strong English proficiency, a keen interest in water electrolysis and green technology, and experience with machine learning tools is advantageous. Impactful scholarly publications and the ability to conduct independent research and collaborate in a team are required. Evidence of English proficiency (IELTS, TOEFL) should be provided if available.

To apply, candidates should contact Prof Chong Meng Nan with a cover letter, CV, and evidence of English proficiency. After confirming suitability, applications can be submitted via the provided link, referencing the research topic as advertised. For further details, consult the application submission guide.