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model with each SNP independently, perhaps adjusting for other covariates such as age and sex. This project will focus on developing and applying novel machine learning and AI methods to improve
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to Fisher-Rao geometry and develop theoretical results characterising MML estimation under various regularity conditions. Aim 2: Development of Computational Methods for MML Design and implement efficient
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, in various formats including images, texts and numeric values. The study of these unstructured data in the pathology laboratory information system (LIS), together with the information from
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: https://www.monash.edu/graduate-research/future-students/scholarships/scholarship-policy-and-procedures . The Opportunity In the face of escalating climate and environmental crises, research in
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systems and swarm robotics. The project builds on well established computational and mathematical modelling techniques to achieve its aims. Departure points will be agent-based simulations, optimisation
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relevant field or equivalent experience Strong administrative, numerical and computing skills Experience with systems like SAP and Callista Exceptional organisational and communication skills A proactive
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Research Training Program (RTP) Stipend Research Training Program (RTP) Scholarships, funded by the Australian Government, support both domestic and international students undertaking Research
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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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become overwhelmed, slower to respond, and more likely to overlook genuine indicators of compromise. A range of methods have been explored to reduce false alerts, including provenance-based analysis and
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methods. In this work we study the effects putative NMDA antagonists xenon, a potent anaesthetic, and nitrous oxide, a weak anaesthetic, on anesthetic-induced changes in brain mechanisms and networks