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for their research and leadership in open-source statistical software, open and reproducible analytics, and analytics for social good. We welcome applications from candidates with expertise in statistical and machine
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and learning transformation agendas. As the successful candidate you will be responsible for providing educational innovation support and advice to staff of the Faculty and work to support a smooth
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leadership in open-source statistical software, open and reproducible analytics, and analytics for social good. We welcome applications from candidates with expertise in statistical and machine learning
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labelling cost, label noise, and selection-time constraints into AL design. O5: Standardised benchmarks: Build or extend a platform (e.g., ALScope-like) for CV + NLP drift settings: open-set, imbalanced, and
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strongly advocates diversity, equality, fairness and openness . We fully support the gender equity principles of the Athena SWAN Charter . The Project Modern video analytics systems must be accurate, privacy
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architectures. Design principled uncertainty modelling frameworks for multi-view/multimodal learning. Model uncertainty propagation across modalities in fusion architectures. Develop robust learning methods under
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As education systems increasingly adopt AI to support teaching and learning, the automation of assessment and feedback processes has emerged as a critical area of innovation. Large-scale learning
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. Selection criteria: Prospective applicants must satisfy Monash University PhD entry requirements as outlined in the Monash Graduate Research Admission Procedure, including: Open to domestic candidates
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Level 7 plus 17% employer superannuation) Identified role. Open to Indigenous applicants only Join our inclusive, collaborative community Be surrounded by extraordinary ideas - and the people who discover
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machine learning can be used to understand complex behaviours such as reliability, competence, transparency and trust. The position will design and develop innovative reasoning algorithms and knowledge