Project summary

Industrial infrastructure around the world depends on protective coatings to prevent corrosion and extend the life of critical steel assets. However, coatings degrade in complex and highly variable ways, making it difficult for industry to accurately predict when maintenance is required or when failure is likely to occur. Understanding and predicting coating degradation is a major engineering challenge with significant implications for infrastructure reliability, safety, sustainability, and cost.

This PhD project will join a multidisciplinary team of researchers applying the following techniques to assess remaining service life of coating systems. The research spans several complementary areas, and candidates are not expected to have expertise across all domains. Depending on the candidate’s skills and interests, the project may involve one or more of the following:

• Drone and sensing technologies Machine learning and AI
• Image analysis
• Polymer/material science
• Statistical modelling
• Electrochemistry
• Asset management principles

The project offers an exciting opportunity to work at the intersection of materials science, corrosion engineering, data analysis, and predictive modelling, with strong relevance to real-world industrial applications. Outcomes from the research will support smarter maintenance strategies, improve asset management decision-making, and contribute to the design of more sustainable and reliable infrastructure systems. Candidates will gain experience with advanced experimental techniques, interdisciplinary research methods, and industry-focused problem solving in an area of growing national and international importance.

Summary of position

The opportunity is for an Australian domestic student to pursue a PhD in an exciting area of research and industry relevance. The offer is open to material scientists/engineers or data analysts to work in an established team of researchers and applying there work to some of Australia's largest and most expensive assets.