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Field
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key part in supporting Australia’s transition to green ironmaking by developing a quantitative framework that connects impurity content, process parameters and microstructural evolution to scalable, low
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and next‑generation manufacturing, yet their performance is fundamentally limited by our inability to precisely control particle alignment and microstructure during fabrication. Existing methods—such as
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for enhanced durability, performing microstructural analysis and mechanical testing. Topology Optimization & AI Integration: Use AI and machine learning to guide structural and topology optimization, creating
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, the project will establish predictive frameworks capable of optimising process parameters, controlling microstructure evolution, and improving part quality in real time. Robotic platforms will be used
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the final weld. Microstructural simulation of grain sizes and morphology after solidification will be developed together with simulation of metallurgical phase transformations at microscale. Combining
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to close-to-magnet particle doses. The stability of a given austenite grain is governed by local microstructural parameters such as the local chemistry, grain size, orientation, the surrounding microstructure
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microstructure, porosity and pore architecture with mechanical properties, CO₂ uptake, carbonation efficiency and CO₂/N₂ selectivity, including the use of adsorption isotherm modelling (e.g. Langmuir) and simple
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valorisation, degradation kinetics & durability, mineralogical phase characterisation of materials, lab-based testing on electrochemical techniques, microstructure performance evaluation and computational
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PhD student at ILL on Advanced Neutron Imaging for Defect Mapping in Repaired Aero-Engine Components
many conditions governing microstructural evolution and defect formation, yet the physics linking process conditions to three-dimensional defect distribution in engineering-scale geometries remains
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cladding processes, including parameter optimisation and advanced monitoring techniques. Develop and refine process models, simulations, and automation strategies. Characterise microstructure, wear behaviour