Monash University Malaysia.
1 month ago
PhD in Machine Learning for In Situ Materials Characterisation Monash University in Malaysia
Degree Level
PhD
Field of study
Computer Science
Funding
Fully funded PhD scholarship available for an outstanding domestic or international candidate. Stipend is AUD 41,100 per annum, tax free (2027 rate). Duration is 3.5 years full time.
Country
Malaysia
University
Monash University Malaysia.
Apply for this position
You'll sign in first, then continue in your ApplyKite dashboard.
Keywords
Suggested positions
About this position
PhD opportunity at Monash University in Machine Learning for In Situ Materials Characterisation, hosted by the Department of Materials Science and Engineering at the Clayton campus in Melbourne, Australia.
This project sits at the interface of materials science, machine learning, computer vision, scientific machine learning, and metallurgy. The successful candidate will work with experimental datasets from electron microscopy, EBSD, X-ray diffraction, X-ray imaging, and other in situ characterisation techniques to learn physically meaningful representations of microstructure, crystallography, phase evolution, chemistry, and morphology.
Research directions may include representation learning, graph neural networks, multimodal learning, and quantitative modelling of the relationships between processing, material evolution, microstructure, and properties. Applications are expected to focus on metals, phase transformations, materials processing, and emerging low-emission metallurgical processes.
The student will join the Metallurgy and Corrosion Research Cluster and benefit from Monash facilities in electron microscopy, X-ray characterisation, in situ materials analysis, high-temperature experimentation, and scientific computing.
Funding: fully funded PhD scholarship for an outstanding domestic or international candidate, with a stipend of AUD 41,100 per annum tax free (2027 rate). Duration is 3.5 years full time.
Eligibility: backgrounds in Materials Science, Metallurgical Engineering, Mechanical Engineering, Physics, Computer Science, Data Science, Chemical Engineering, or a closely related discipline are welcome. Prior experience with ML, Python, SEM/EBSD, XRD, image analysis, microstructure characterisation, or materials modelling is desirable, but applicants do not need to be experts in both ML and materials science. Applicants who already hold a PhD are not eligible.
How to apply: email Dr Yuxiang Wu with your CV, academic transcripts, and a brief description of your research interests before submitting an Expression of Interest. The project is listed as applications accepted all year round, with a flexible 2027 start by agreement.
Funding details
Fully funded PhD scholarship available for an outstanding domestic or international candidate. Stipend is AUD 41,100 per annum, tax free (2027 rate). Duration is 3.5 years full time.
What's required
Applicants should have a background in Materials Science, Metallurgical Engineering, Mechanical Engineering, Physics, Computer Science, Data Science, Chemical Engineering, or a closely related discipline. Strong interest in machine learning, materials characterisation or computational materials science is expected, along with strong quantitative and problem-solving skills, capacity for independent self-motivated research, and excellent communication and teamwork skills. Previous experience with machine learning, computer vision, graph neural networks, Python, SEM/EBSD, XRD, image analysis, microstructure characterisation or materials modelling is desirable. Applicants who already hold a PhD degree will not be considered.
How to apply
Email Dr Yuxiang Wu at [email protected] with your CV, academic transcripts, and a brief description of your research interests before submitting an Expression of Interest. Then register your interest for the project through FindAPhD.
Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.
Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.