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catastrophically so. This PhD will develop technologies for addressing this serious problem, building upon our groundbreaking research into the problem . Required knowledge A solid grounding in machine learning
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testing approaches that can be used to verify that machine learning models are not biased. Required knowledge Software engineering, software testing, statistics, machine learning
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People with disabilities are excluded from the assistive technology creation process because the methods and tools that are used are inaccessible. This leads to missed opportunities to create more
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practice-based PhD program, please see: https://sensilab.monash.edu/work-with-us/practice-based-phd/
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This PhD project aims to mitigate the data scarcity of new NLP and Multimodal applications by developing novel active learning algorithms. In this project, the student will leverage large foundation
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search is guided simultaneously by multiple contrasting objectives: maximising the QA resources on software modules that are risky, severe, and affect a large number of end-users, while minimising the cost
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Explainable AI or XAI is essential. We will use a variety of XAI methods, such as Grad-CAM, and others. This project will involve a lot of experiments using DL/AI methods. We will use the Monash High
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The project develops methods to use acoustic data for the identification of animals in the wild and in controlled settings. It is part of a broader effort to build AI-enabled methods to support
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Species’ distributions are shifting in response to global climate change and other human pressures. Accurate methods to monitor and predict distribution shifts are urgently needed to manage
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. Recent works on knowledge graph question generation [4,5] have mainly focussed on multi-hop questions. This project aims at developing novel methods that jointly address the challenging, dual problem