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Field
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in conventional imaging, and to access a complementary ‘dark-field’ signal that originates from tiny sample structures. We do this by designing and implementing novel experimental set-ups and
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biological networks as a form of relational and structural learning. Given a network dataset, we wish to infer a model of the distribution of the elements of this data-set, possibly as a mixture of several
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experiencing financial hardship enrolled in or applying for the Bachelor of Built Environment (Honours) (Construction Management and Quantity Surveying). This scholarship is not available to: single-unit study
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I am an ARC Future (former DECRA) Fellow and lead the Structured Nanophotonics Group at Monash University. My research in nanophotonics explores the full potential and multi-dimensional nature
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discovery. While deep generative models have shown promise in proposing novel molecular structures, they typically require massive, cleanly labelled datasets to train effectively. In practice, acquiring high
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. This is a greenfields opportunity to design and deliver the structure, framework, and processes that will underpin position management across UWA. The role will establish clear and consistent approaches
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extract events and mine knowledge from existing unstructured/structured data, and exploit the knowledge via neuro-symbolic reasoning for crime prevention (eg -sexual assaults), especially when there is no
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they bond in materials, but also develop transferable skills in scientific computing, data analysis and visualisation. "Machine learning for atomic-scale structure determination in thick nanostructures" (with
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reticulum and theGolgi apparatus. More than 50% of all proteins within the cell undergo such modification. When bound to proteins, they affect their structure, function, stability, folding, half-life
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networks so that they can accurately identify anomalies in the presence of concept drift. We would like to consider different types of changes in graph structures, such as emergence/deletion of new nodes