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                of Materials Science and Engineering (MSE), Faculty of Engineering, Monash University. This PhD project will contribute to MSE’s strategic research initiative on accelerating Australian green ironmaking 
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                the Department of Materials Science and Engineering (MSE), Faculty of Engineering, Monash University. This PhD project forms part of the Baosteel–Australia Joint Centre (BAJC) collaboration and will investigate 
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                Intelligence; Software Systems & Cybersecurity; and Human-Centred Computing - with cross-cutting research themes covering work from core computing science through software engineering to social and 
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                , reduce resource waste, and create scalable mental health interventions, advancing national sustainability and education priorities. Value • Stipend of AUD $47,020 • Maximum period of tenure of an award is 
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                field” imaging techniques to solve many important problems in biology and change clinical practice in respiratory medicine. Our ongoing research program involves developing new imaging technologies 
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                the areas of experimental and theoretical physics, synthetic, physical and computational chemistry, material sciences and related areas. The Opportunity The OPTEXC IRTG involves 20 academics in Australia and 
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                the value of the spent battery materials. Student type Future Students Faculties and centres Faculty of Science & Engineering Western Australian School of Mines (WASM) Course type Higher Degree by Research 
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                ) and complete any other CSIRO requirements. Ideally, the applicant will have a bachelor degree in chemistry, chemical engineering, material science or engineering or relevant fields. Preference will be 
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                materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for 
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                materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models