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Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling
-microscopy, proteomics and metabolomics. This project will study large protein secretion systems, drug-efflux pumps and lipid transport towers to provide means to integrate spatial information and metabolic
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battery systems or distributed energy resources. This will include benchmarking data models, and deploying and testing these techniques in real-world, large-scale deployments. Contributing to setting
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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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This scholarship is jointly funded by a leading computer vision company in Australia and the STEM College of RMIT University. The research includes investigation into automated product quality
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Autonomous methods for fault or anomaly detection and classification of PV plants with high accuracy are necessary for the monitoring of large-size PV power plants. Objectives also include other
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with a systems biology approach in which we will investigate whether the immunological changes in young and elderly are characterized with changes in the gut microbial composition. Currently big data
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Status: Closed Applications open: 27/05/2025 Applications close: 27/06/2025 View printable version [.pdf] About this scholarship Description/Applicant information The National Drug Research
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Successful candidates will be expected to have a solid background in finance and sustainable finance, research methods, strong data analytic skills, and experience working with large datasets. RMIT
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July 2025. Integrated PhD Program entry requirements and scholarship eligibility Applicants should have a 4-year bachelor degree or master's degree (any length) in economics, econometrics or a related
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in medicine — and how we can stop them being misused to spread health disinformation or pose risks to patients. You’ll contribute to one of the world’s leading programs studying large language models