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applied physics other related disciplines. Demonstrated knowledge in at least one of the following areas: porous media flow computational fluid dynamics (CFD) pore-network modelling lattice Boltzmann method
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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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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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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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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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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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(e.g., Chemical, Materials, Environmental, Resources) or Chemistry (e.g., Inorganic Chemistry, Physical Chemistry, Electrochemistry) or equivalent degree, and meet the language requirement (IELTS