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of the financial details section. About the scholarship Health AI is being adopted across health services at unprecedented speed and scale. Early evidence suggests gains in efficiency and clinician experience, but
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settings to improve accuracy and confirm the system is fit for purpose. This PhD project will develop, validate and refine a saliva-based hydration assessment system for use in sport and workplace settings
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Status: Closed Applications open: 5/09/2024 Applications close: 31/12/2025 View printable version [.pdf] About this scholarship Description/Applicant information This scholarship is funded by CSIRO
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PhD Scholarship Opportunity – Creating Evidence-Based Tools to Help Injured Workers Navigate Income Support Systems Job No.: 695315 Location: 509 St Kilda Road, Melbourne (as of September 2026
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isn’t like other places you’ve worked. We are a large organisation with a diverse and highly talented community doing extraordinary work. We are driven to be thoughtful, practical and purposeful in all we
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What you'll receive You'll receive a stipend scholarship of $32,500 per annum for a maximum duration of 3.5 years while undertaking a QUT PhD. This is the full-time, tax-free rate. If you're
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What you'll receive The CSIRO Industry PhD Program (iPhD) is a four-year research training program, focusing on applied research that benefits industry by solving real-world challenges. It aims
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Local students applying for a place in a higher degrees by research (HDR) program will automatically be considered for an Australian Government Research Training Program (RTP) Fees-Offset (Domestic) scholarship. Courses eligible Doctor of Philosophy (PhD), Doctor of Psychology, Master by...
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language processing, large language models, machine learning, network analysis, social media analytics, and large-scale analysis of online discourse and communities. This PhD scholarship will be based within
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datasets with different configurations (e.g., number of channels, sampling frequency and resolution). To leverage large-scale self-supervised learning to train models on unlabeled EEG data, reducing reliance