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information on our practice-based PhD program, please see: https://sensilab.monash.edu/work-with-us/practice-based-phd/
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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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Anomaly detection methods address the need for automatic detection of unusual events with applications in cybersecurity. This project aims to address the efficacy of existing models when applied
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Time series are an ever growing form of data, generated by numerous types of sensors and automated processes. However, machine learning and deep learning methods for analysing time series are much
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development — core technologies for the next decade of AI-powered engineering. This is a collaborative research project with Atlassian. Check out some recent work: - https://www.atlassian.com/software/rovo-dev
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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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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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field” imaging techniques to solve many important problems in biology and change clinical practice in respiratory medicine. Our ongoing research program involves developing new imaging technologies
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provide multiple competitive scholarships funded by a national elite HDR training program: Data61 Next Generation Graduate (https://www.csiro.au/en/work-with-us/funding-programs/programs/next-generation
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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