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Monash Silver Jubilee PhD Scholarship The Monash Silver Jubilee Scholarship (MSJS) is available to help support a graduate research student with living costs whilst studying at Monash. It is
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RMIT’s PhD entry requirements. To be eligible for this scholarship you must: have first-class Honours or 2A Honours or equivalent in a relevant discipline of electrochemistry, chemistry or material science
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Transcript English language proficiency test result (for international applicants) Applications are now open. Applications are now open. Application will close when the PhD candidate is selected. Application
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: asgar.farahnaky@rmit.edu.au We have an industry focused project on Transformation of Plant Protein Processing Waste into High Value Health Promoting Food Ingredients, the PhD student will work with and supported by
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. Applications now open. Applications now open. Applications will close when the PhD candidate is selected. Applications will close when the PhD candidate is selected. This project will implement DRL technique
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) before the human eye can see them. The principal aim of this PhD research program is to develop methods to improve the hyperspectral image classification using deep learning techniques. The developed
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The PhD candidate will gain intensive knowledge in innovative processing protocols for chemical sensing and to develop data acquisition system with the Machine Learning (ML) and/or Deep Learning (DL
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-class or H1 honours degree in Computer Science, Engineering or equivalent in a related discipline. Applicants must satisfy RMIT University’s PhD entry requirements. Applicants must have a first-class or
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As a pregnancy approaches term (the point at which the foetus is considered fully developed), decisions are made about the timing of birth and the way babies are born. These decisions are incredibly challenging for clinicians and pregnant women. Digital health records, advances in big data,...
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models to identify patterns and structures in the learnt features that correlate with high-level semantic concepts. • Objective 2: Evolution of Learnt Features -- Analyse the hierarchical progression