PhD in Machine Learning for In Situ Characterisation of Phase Transformation in Green Metals Monash University in Australia

This fully funded PhD opportunity at Monash University’s Department of Materials Science and Engineering focuses on the intersection of machine learning and advanced in situ X-ray characterisation for phase transformation in green metals. The project, titled 'Processing Intelligence for Green Metals Using In Situ X-ray Characterisation and Machine Learning,' aims to develop new physical-digital capabilities for minerals-to-green metals transformation, supporting low-emission metals production and recycling.

As a PhD student, you will develop and apply in situ X-ray characterisation methods to capture time-resolved structural, chemical, and microstructural changes during minerals-to-metals processing. The research encompasses aqueous, thermal, and hybrid pathways relevant to extraction, refining, recycling, phase transformation, impurity evolution, and microstructure development. You will use advanced X-ray techniques—including diffraction, scattering, imaging, and multimodal characterisation—to generate data-rich descriptions of evolving materials and processing pathways.

A central aspect of the project is coupling experimental data with machine learning, mechanistic modelling, and automated data analysis to extract processing-structure-property relationships and support predictive process optimisation. The research environment is multidisciplinary, spanning materials engineering, X-ray science, and artificial intelligence, with collaboration across these fields.

Supervision is provided by Dr Yuxiang Wu and Professor Michael Preuss, both experts in materials science and engineering. The project is based at Monash’s Clayton campus in Melbourne, Australia, and offers a stipend of AUD 37,145 per annum (tax free, 2026 rate) for 3.5 years full-time. Scholarships are available for both domestic and international applicants, subject to Monash eligibility and competitive selection.

Applicants should have a strong background in Materials Science, Metallurgical Engineering, Mechanical Engineering, Physics, Chemical Engineering, Data Science, or a closely related discipline. Essential qualities include a strong interest in phase transformations, process chemistry, materials processing, and advanced X-ray characterisation, as well as experience or enthusiasm for machine learning, scientific computing, automated data analysis, or computational modelling. Excellent communication, interpersonal, teamwork, and problem-solving skills are expected. Preference will be given to candidates who graduated in the top 10% of their cohort, from a well-ranked university, have authored peer-reviewed research publications, and possess excellent written and spoken English.

Eligibility requires meeting Monash PhD entry and English language requirements. Candidates who already hold a PhD are not eligible. Applicants should have completed, or be in the process of completing, a Bachelor’s H1 Honours degree, or already hold an H1E Bachelor’s and/or Master’s degree. Candidates in the process of completing their H1 degree will be considered.

Applications are accepted year-round. To apply, review the PhD entry requirements and Monash English Language Proficiency requirements, then contact Dr Yuxiang Wu at yuxiang.wu@monash.edu with your CV, academic transcript, and a brief statement outlining your interest in the project. Supervisor support is required before submitting an Expression of Interest. If the project fit is confirmed, submit an Expression of Interest. Upon receiving an Invitation to Apply, submit your formal application via the Monash online application portal.

For further information, visit the Monash Engineering graduate research page or contact the Graduate Research Office at eng-gradresearch@monash.edu.