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Automating code generation, SQL query formulation, and data preprocessing pipelines is a crucial step toward intelligent and efficient software development. This project aims to leverage large
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. Application of artificial intelligence/machine learning to the big data from genetics and omics is well recognized in healthcare, however, its application to the data reported everyday as part of the clinical
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welcome applicants with a background in health science (including medicine), biomedical imaging, biomedical engineering, radiation sciences, AI, or big data, who can bring fresh perspectives and drive
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, and costs of running diverse applications in large-scale distributed systems. This project offers researchers and students a chance to explore cutting-edge concepts in AI-driven infrastructure
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specific and timely content recommendations by service providers to enhance user engagement. Analysing website activity data to identify potential crisis points that might require intervention or escalation
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The proliferation of misinformation and disinformation on online platforms has become a critical societal issue. The rapid spread of false information poses significant threats to public discourse
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: Experience in physical system modelling including finite element modelling Experience working with large codebases in open source software environments Proficient user of HPC environments including MPI
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and carrying materials) research material in a format other than ordinary print (i.e. large print, audio-type) communication in other than oral mode (i.e. written, interpreter, computer with voice
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Skip to main content Main Menu - Primary Home Projects Supervisors Expression of Interest Contact Faithful and Salient Multimodal Data-to-Text Generation Primary supervisor Teresa Wang Co-supervisors
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Research data governance is an under-explored issue, and technical infrastructures to support the transparency and control of data collected in human research studies (from medicine to social