Doctor of Philosophy (PhD) in Machine Learning for In Situ Characterisation of Phase Transformation in Green Metals

About the Project

Doctor of Philosophy (PhD) in Machine Learning for In Situ Characterisation of Phase Transformation in Green Metals

Host: Department of Materials Science and Engineering, Monash University

Location: Clayton campus, Melbourne, Australia

Duration: 3.5 years, full time

Supervisors: Dr Yuxiang Wu (yuxiang.wu@monash.edu) and Professor Michael Preuss (michael.preuss@monash.edu)

Start: July 2026 or later

Stipend: AUD 37,145 per annum, tax free, 2026 rate

Course code: 3291 Doctor of Philosophy (PhD)

Scholarship status: A scholarship opportunity may be available for this project.

Project overview

Expressions of interest are sought from outstanding candidates for PhD study in the Department of Materials Science and Engineering within the Faculty of Engineering.

The project, Processing Intelligence for Green Metals Using In Situ X-ray Characterisation and Machine Learning, will develop advanced in situ X-ray characterisation and machine-learning-enabled processing intelligence for green metals transformation.

The project will focus on capturing time-resolved structural and chemical changes during minerals-to-metals processing, including aqueous, thermal, and hybrid pathways relevant to extraction, refining, recycling, phase transformation, impurity evolution, and microstructure development.

You will use advanced in situ X-ray methods, including diffraction, scattering, imaging, and complementary multimodal characterisation, to generate data-rich descriptions of evolving materials and processing pathways. A central aim is to couple these experiments with machine learning, mechanistic modelling, and automated data analysis to extract processing-structure-property relationships and support predictive process optimisation.

The project sits within Monash’s broader ambition to build physical-digital capability for infrastructure platforms in minerals-to-green metals transformation. It will suit a candidate interested in combining experimental materials science, advanced characterisation, and data-driven modelling to develop new forms of processing intelligence for low-emission metals production and recycling.

You will be supervised by Dr Yuxiang Wu and Professor Michael Preuss, with collaborators across materials engineering, X-ray science, and artificial intelligence.

What you will do

• Develop in situ X-ray characterisation methods for green metals processing.
• Capture time-resolved structural, chemical, and microstructural changes during minerals-to-metals transformation.
• Investigate phase transformation, impurity evolution, and microstructure development during aqueous, thermal, and hybrid processing pathways.
• Use advanced X-ray techniques, including diffraction, scattering, imaging, and complementary multimodal characterisation.
• Apply machine learning, mechanistic modelling, scientific computing, and automated data analysis to extract processing-structure-property relationships.
• Support predictive process optimisation for low-emission metals production and recycling.
• Work within a multidisciplinary research environment spanning materials engineering, X-ray science, and artificial intelligence.

Candidate profile

Applications are invited from candidates with backgrounds in one or more of the following: Materials Science, Metallurgical Engineering, Mechanical Engineering, Physics, Chemical Engineering, Data Science, or a closely related discipline.

You should demonstrate:

• Strong interest in phase transformations, process chemistry, materials processing, and advanced X-ray characterisation.
• Experience or enthusiasm for machine learning, scientific computing, automated data analysis, or computational modelling.
• Capacity for independent, self-motivated research.
• Excellent communication, interpersonal, teamwork, and problem-solving skills.

Your application will be viewed favourably if you:

• Graduated in the top 10% of your cohort.
• Graduated from a well-ranked university.
• Have authored peer-reviewed research publications.
• Possess excellent written and spoken English.

Eligibility

Applicants must meet Monash PhD entry and English language requirements. Candidates who already hold a PhD are not eligible.

Applicants should have either 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 who are in the process of completing their H1 degree will be considered.

How to apply

Instructions on applying for a PhD with Monash Engineering:

Check eligibility

Review the PhD entry requirements and Monash English Language Proficiency requirements.

Contact the supervisors

Email 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.

Submit an Expression of Interest

If the project fit is confirmed and supervisor support is provided, submit an Expression of Interest.

Invitation to Apply

If your Expression of Interest is supported, you will be supplied with an Invitation to Apply.

Lodge the formal application

After receiving an Invitation to Apply, submit your formal application via the Monash online application portal.

Funding Notes

This is a fully funded PhD project. Scholarships are available for both domestic and international applicants. Applicants may be Australian citizens, Australian Permanent Residents, New Zealand citizens, or international candidates holding or eligible to obtain a valid student visa. Scholarship is attached to the project and subject to Monash eligibility and competitive selection.