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supervisors, read about our research strengths and initiate an application, all in one place. Browse Research projects Honours and Masters project Supervisors Login Recently added GEMS 2026: Toward Distribution
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"A picture is worth a thousands words"... or so the saying goes. How much information can we extract from an image of an insect on a flower? What species is the insect? What species is the flower? Where was the photograph taken? And at what time of the year? What time of the day? What was the...
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their performance evaluated in terms of classification accuracy, computational speed, and overall usability. Required knowledge Deep learning (CNNs, Transformers) and computer vision Knowledge distillation for model
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spectroscopy and Gaia data of star clusters to decipher the mystery of the Lithium-rich giant stars" (with Prof John Lattanzio) "The origin of the heavy elements: Computer simulations of neutron-capture
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Asahi Study Grant Monash Travel Support Grant The Asahi Study Grant is a $6000 travel scholarship created to support students in the University’s Japanese Studies Program to attend a study tour to
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learning approaches to enable multi-site collaboration while preserving patient privacy. This ensures more generalized and reliable reconstruction models that can adapt to diverse data distributions
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Master of Commerce Scholarship Sir John Monash Fee Scholarship The Master of Commerce is a research-oriented programme preparing students for a PhD and an academic career. Offered as part of
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. Oversee accreditation processes for the medical program, working closely with clinical and pre-clinical leads, curriculum heads, and assessment teams. Chair the admissions and selection reference group
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that are constructed in a way that is inspired by what we know about self-awareness circuits in the brain and the field of self-aware computing. The project will advanced state of the art AI for NLP or vision or both
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at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need