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Engineering, Solid-State Physics, Chemical Engineering, or a closely related discipline. Applicants must demonstrate: A strong interest in thermodynamics, kinetics, and microstructure evolution in materials
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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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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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is pioneering the development of a SaaS platform based at LotFourteen that leverages AI to help homeowners find the best balance in design and material selection for building resilient homes
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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
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recovery rate in the LIB recycling process and reduce waste and carbon footprint in the battery value chain. By returning waste to battery manufacturing, the project will make the battery value chain more
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Engineering (e.g., Chemical, Materials, Environmental, Resources) or Chemistry (e.g., Inorganic Chemistry, Physical Chemistry, Electrochemistry) or equivalent degree, and meet the language requirement (IELTS
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should have Masters or bachelor (Honors) degree in Engineering (e.g., Chemical, Materials, Environmental, Resources) or Chemistry (e.g., Inorganic Chemistry, Physical Chemistry, Electrochemistry
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partner a professional development training program. A tuition fee scholarship for four years. Scholarships also include up to $1,500 relocation allowance. Supervisor: Prof Paul Dastoor Available