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

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 (

yuxiang.wu@monash.edu ) in a multidisciplinary environment spanning materials science, advanced characterisation, computational modelling and artificial intelligence.

Machine Learning, Philosophy, Machine Learning, Philosophy, Scholarship, Problem Solving Skills, Material Science, Materials Engineering, Crystallography, Training Data Sets, * 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., * 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., * 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.

Monash Engineering is a leading global centre for research consistently placing highly in international ranking lists. We engage with industry and other world leading institutes to carry out pioneering research that benefits society and changes the world around us. Our world-class researchers are driven by a passion and commitment to leaving a more sustainable legacy for future generations.

We are committed to training the next generation of research leaders through our graduate research degrees. Our students have the opportunity to make an impact and solve real world issues in a stimulating, supportive environment with access to cutting edge facilities., Monash University recognises that its Australian campuses are located on the unceded lands of the people of the Kulin nations, and pays its respects to their elders, past and present.