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PhD Scholarship in Big Data and Analytics for Crop Genetics Modern agriculture and biomedical research are driven by the availability of big data and the development of data science. The recent
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, and mortality. This will be achieved by linking large existing datasets that contain data on people with diabetes foot disease in the community (such as Diabetes Registries, Diabetes Foot Registries
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Charles Sturt University’s AI and Cyber Futures Institute (AICF), a cutting-edge research institute of excellence in data science, artificial intelligence, and cyber security, is poised to showcase
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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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or unethical responses, can be manipulated into producing them when provided with a large enough number of well-crafted “in-context” examples. This raises critical questions: Why do AI models adapt so
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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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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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Status: Closed Applications open: 1/07/2024 Applications close: 18/08/2024 View printable version [.pdf] About this scholarship Description/Applicant information Project Overview A major
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data requirements, and lower costs for large-scale modelling tasks. PINNs enhance predictive capabilities and efficiency by combining data-driven methods with physical principles. Unlike traditional
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of better prevention and public health policies in rural and remote areas. This position will contribute to the design of qualitative research components, coordinate qualitative data collection from rural