Monash University 
Clayton, Australia Resumo da oferta
Monash University invites applications for a fully funded PhD position in Machine Learning for In Situ Materials Characterisation at its Clayton campus. This three-and-a-half-year fixed-term role offers an annual tax-free stipend of AUD 41,100 (2027 rate). The project focuses on developing machine learning methods to analyze complex datasets from materials characterisation techniques, such as electron microscopy and X-ray diffraction. Candidates will investigate how AI can learn physically meaningful representations to understand material evolution during processing. Supervised by Dr Yuxiang Wu, the position seeks outstanding candidates with backgrounds in materials science, engineering, computer science, or related fields. Strong quantitative skills and interest in computational materials science are essential. Applications close on April 30, 2027.
Gerado a partir do conteúdo guardado da oferta. Confirme sempre os detalhes no anúncio oficial.
Descrição do projeto
Doctor of Philosophy (PhD) in Machine Learning for In Situ Materials Characterisation
Job No.: 698995
Location: Clayton campus
Employment Type: Full-time
Graduate Research Degree: 3291 - Doctor of Philosophy (PhD)
Hiring: 2027, flexible by agreement
Duration: 3.5-year fixed-term appointment
Remuneration: AUD 41,100 per annum (tax-free stipend) (2027 rate)
Scholarship status: Fully funded PhD scholarship available for an outstanding domestic or international candidate.
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.
The Opportunity
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.
Project Details
Preferred Background
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:
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
To apply for a graduate research degree and/or scholarship at Monash Engineering please follow the following steps:
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