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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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, software, human-computer interaction, ...). We also work very much interdisciplinarily with colleagues from other faculties, e.g. on bio-diversity matters, on physical aspects, on modelling aspects, and on
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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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Automated Program Repair (APR) is the grand challenge in software engineering research. Many APR methods have shown promising results in fixing bugs with minimal, or even no human intervention
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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
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part of the Books for the Vision Impaired and the GraVVITAS frameworks (www.monash.edu/it/inclusive-tech ). The project will employ computer vision, image processing and human computer interaction
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possess translational symmetry, the role of structure and symmetry in glasses is not established. This research programme involves the development of new x-ray and electron diffraction-based methods
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projects that involve data analysis, the application of artificial intelligence, the development of new detection techniques, and the exploration of new experimental methods through collaboration with our
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Discovery Project, this research aims to develop highly novel physics-informed deep learning methods for Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) and applications in image