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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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type Current Students Faculties and centres Faculty of Science & Engineering Western Australian School of Mines (WASM) Course type Higher Degree by Research Citizenship Australian Citizen Australian
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AI & Cyber Futures Institute - Charles Sturt University | Bathurst, New South Wales | Australia | 3 months ago
Herbicides PhD Student Scholarships: Integrated Australian Shorn Wool Production Forecasting System Using Digital Twin Technology PhD Student Scholarships: Artificial Intelligence-Powered Climate Resilience
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characterising and conducting comprehensive evaluations. This includes analysing their mineral composition, identifying the presence of critical or valuable minerals/metals, and determining their recoverability
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(1865 – 1924) was a senior Gynaecologist who spent several years studying at the University of Melbourne, who completed a year in civil engineering before transferring to medicine and then completing his
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burden and symptoms, to compare the cycling biomechanics of those with and without knee pain, and 2) to co-design an intervention in conjunction with cyclists and clinicians to optimise cycling mechanics
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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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or above. have outstanding grounding in one or more relevant disciplines including cancer biology, cancer medicine, physics, chemistry, , engineering, machine learning / data science, coding. How to apply
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Application dates Applications close30 September 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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the development of predictive models to anticipate potential failures. Additionally, the project will facilitate the transfer of this technology to industry, while also advancing academic knowledge