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through their investor conference a co-investment from the College of Science and Engineering. This funding provides a unique opportunity to advance innovative medical device research and support four high
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physical, neurocognitive, emotional, and social impacts—follow-up care remains fragmented and inconsistent across healthcare systems. Our program is developing a comprehensive, consumer-informed, and
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investigate online feature engineering, continual learning and uncertainty quantification. Balance performance with governance. Projects will evaluate risk-adjusted returns alongside interpretability
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/admissions/entry-requirements/minimum . Candidate Requirements Undergraduate honours degree or Master's by research degree in a relevant discipline (Computer Science, Robotics, Engineering or others related
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and technology. Engaging in solving a real-world problem requires a real solution to enhance housing resilience. Applying research skills to optimise and innovate AI-driven solutions for the housing
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manufacturing practices with real-world industry application. This full PhD scholarship is based at Swinburne’s School of Science, Computing and Engineering Technologies in Melbourne. The successful candidate
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, biology, engineering, machine learning / data science, coding. How to apply: This is an Expression of Interest process. To express your interest in applying, candidates must supply the following information
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impact in translational fields, which often aim to use generative AI in a responsible way. This PhD project is part of a larger cohort of projects in the Monash AI Institute, a recently expanded program
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Application dates Applications close26 August 2025 What you'll receive You'll receive a stipend of $37,010 per annum (from 1 January 2026) for a maximum duration of 3.5 years while undertaking a QUT PhD. The duration includes an extension of up to six months (PhD) if approved for your...
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project within an NHMRC-funded program focused on dementia research. Using a systems biology framework, the project will integrate genomic and lifestyle data to identify key molecular pathways and risk