Publisher
source

Monash University Malaysia.

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 flag

Country

Malaysia

University

Monash University Malaysia.

Apply for this position

You'll sign in first, then continue in your ApplyKite dashboard.

Keywords

Computer Science
Materials Science
Computer Vision
Metallurgy
Material Characterization
Microstructure Analysis
Phase Transformation
ML

Suggested positions

Publisher
source

Universiti Kebangsaan Malaysia (UKM)

Country flag

Malaysia

Fully funded PhD opportunity at Universiti Kebangsaan Malaysia (UKM) , Faculty of Engineering & Built Environment, through the Microwave Technology Research Group (MTRG) . This doctoral project focuses on microwave sensing , microwave imaging , and machine learning applications in microwave engineering. It is a strong fit for candidates interested in advanced electrical engineering research, sensor technology, imaging systems, and computational methods for engineering innovation. Funding: Gradua...

Computer Science
Electrical Engineering
Microwave Engineering
+1 more

2 weeks ago

Publisher
source

University of Arizona

Country flag

United States

Our group at the University of Arizona is accepting applications for a postdoc in structural materials informatics . The position sits at the intersection of the MSE Department , the NSF Center for New Frontiers of Sound , and the Graduate Program in Applied Mathematics , offering a highly interdisciplinary environment with industry partnerships and access to unique industrial samples. The research combines non-destructive evaluation of structural materials with experimental characterization of ...

Computer Science
Signal Processing
Mechanical Engineering
+7 more

1 month ago

Publisher
source

Monash University Malaysia.

Country flag

Malaysia

Monash University’s Department of Materials Science and Engineering is advertising a funded PhD project in physical metallurgy focused on hydrogen-based iron oxide reduction for low-carbon and green ironmaking. The project is based at the Clayton campus in Melbourne, Australia , and is hosted by the Metallurgy and Corrosion Research Cluster . The research will examine how phase transformations , reduction pathways , and microstructure evolution interact during the conversion of iron oxides to me...

Environmental Science
Mechanical Engineering
Chemical Engineering
+7 more

1 month ago

Publisher
source

University of Southampton

Country flag

United Kingdom

PhD opportunity at the University of Southampton in circular lifecycle knowledge for aerospace titanium . This project sits at the intersection of Materials Science , Aerospace Engineering , Mechanical Engineering , and Computer Science . The research aims to create a traceable pathway from titanium scrap to requalified aerospace-grade feedstock using advanced powder metallurgy, materials passports, lifecycle data, AI/ML approaches, ontology design, and metadata schemas. You will work on titaniu...

Computer Science
Mechanical Engineering
Materials Science
+4 more

2 weeks ago

Publisher
source

Hoi Leong Lee

Universiti Malaysia Pahang

Country flag

Malaysia

Universiti Malaysia Perlis is advertising MSc and PhD research opportunities in an industry-funded project focused on Artificial Intelligence , Machine Learning , Deep Learning , Computer Vision , Image Processing , and Medical Image Analysis . The post welcomes applicants with backgrounds in Electronic Engineering , Computer Engineering , Biomedical Engineering , Computer Science , Artificial Intelligence , Data Science , or related disciplines. Both full-time and part-time study modes are avai...

Computer Science
Data Science
Biomedical Engineering
+9 more

1 month ago

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.

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts and confirm requirements with the university.