Doctor of Philosophy (PhD) in Machine Learning for In Situ Materials Characterisation Full-time

Doctor of Philosophy (PhD) in Machine Learning for In Situ Materials Characterisation

Applications are invited from outstanding candidates for PhD study in the Department of Materials Science and Engineering within the Faculty of Engineering.

Project title: Machine Learning for In Situ Materials Characterisation.

We are seeking an outstanding PhD candidate to develop machine learning methods for advanced and in situ materials characterisation. Modern characterisation techniques generate increasingly complex datasets describing microstructure, crystallography, phase evolution, chemistry and morphology. This project will investigate how machine learning can learn physically meaningful representations directly from these data and help understand how materials evolve during processing and phase transformation.

The candidate will work with experimental datasets from techniques such as electron microscopy, EBSD, X-ray diffraction, X-ray imaging and in situ characterisation. Depending on the candidate's background and research direction, approaches may include computer vision, representation learning, graph neural networks, multimodal learning and scientific machine learning.

A central aim is to develop quantitative relationships linking processing, material evolution, microstructure and properties. Applications will focus primarily on metals, phase transformations and materials processing, including emerging low-emission metallurgical processes.

The candidate will be supervised by Dr Yuxiang Wu in a multidisciplinary environment spanning materials science, advanced characterisation, computational modelling and artificial intelligence.

Project details:

• Start: 2027, flexible by agreement
• Graduate research degree: Doctor of Philosophy (PhD)
• Location: Clayton campus
• Department: Materials Science and Engineering
• Fully funded PhD scholarship available for an outstanding domestic or international candidate, subject to formal Monash assessment and approvals.

Preferred background:

• Materials Science, Metallurgical Engineering, Mechanical Engineering, Physics, Computer Science, Data Science, Chemical Engineering, or related disciplines.
• Strong interest in machine learning, materials characterisation or computational materials science.
• Previous experience with machine learning, computer vision, graph neural networks, Python, SEM/EBSD, XRD, image analysis, microstructure characterisation or materials modelling is desirable.

Candidate requirements:

Applicants will be considered provided they fulfil the criteria for PhD admission at Monash University. Details of the relevant requirements are available at www.monash.edu/engineering/future-students/graduate-research/how-to-apply.

Your application will be looked upon favourably if you:

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

Note: applicants who already hold a PhD degree will not be considered.

Applicants must show strong quantitative and problem-solving skills, excellent communication and teamwork skills, and the capacity to conduct self-motivated research. Candidates are not expected to already be experts in both machine learning and materials science; strong candidates from either background who are interested in working across the interface are encouraged to apply.

Application process:

• Check your eligibility, including the PhD entry requirements and Monash's English Language Proficiency requirements.
• You may contact Dr Yuxiang Wu by email with your CV, academic transcript, and a brief statement outlining your interest in the project.
• Submit a full application through My.App at myapp.monash.edu/s, listing Dr Yuxiang Wu as your preferred supervisor.
• You are not required to submit an Expression of Interest or receive an invitation to apply before submitting your My.App application.

Enquiries:

For enquiries about the project, please contact Dr Yuxiang Wu at yuxiang.wu@monash.edu.

Further enquiries about the scholarship or application process should be directed to the Graduate Research Office at eng-gradresearch@monash.edu or visit www.monash.edu/engineering/future-students/graduate-research.

Applications close: Friday 30 April 2027, 11:55pm AEST