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I specialise in the numerical modelling of high-energy particle collisions , such as those occurring at the Large Hadron Collider. Accordingly, most projects I offer straddle the intersection
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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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aims to explore who takes physics and astrophysics major units, why they pursue them, and what obstacles they may face. There are a number of research questions under this umbrella. Computational
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analysis, contextual analysis, audio feature extraction, and machine learning models to identify and assess potentially dangerous content. Similarly, computer vision models are implemented to analyse images
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events with the GOTO telescope network. Projects focussing on thermonuclear bursts will involve analysis of new and archival data from satellite-based X-ray telescopes, and running numerical models
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tissues or reveal micro- or nano-structural features, like the small air sacs in lungs. To overcome these limitations, alternative X-ray imaging methods have been developed: X-ray phase-contrast and dark
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methods dealing with model complexity - e.g., AIC, BIC, MDL, MML - can enhance deep learning. References: D. L. Dowe (2008a), "Foreword re C. S. Wallace", Computer Journal, Vol. 51, No. 5 (Sept. 2008
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privacy constraints, robust solutions are essential. This PhD project will develop methods for building reliable medical imaging models that generalize across distribution shifts without retraining
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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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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