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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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. This work combines computational modelling and simulation with biological experiments that are analysed using cutting-edge computer vision techniques. We collaborate closely with Macquarie University where
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Metallurgy and Corrosion cluster, working within a multidisciplinary team spanning theory, advanced characterisation, and computational modelling. This environment provides an excellent platform for developing
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, computational modelling, and data-driven alloy design to: Understand the mechanisms of local austenite-to-ferrite transformation in low-alloy steels; Develop frameworks to predict and control
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Monash Leaders Scholarships Monash Leaders Scholarships are awarded to applicants that demonstrate leadership and commitment to give back to the community through the Access Monash Mentoring program
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classification'', Computer Journal, Vol 11, No 2, August 1968, pp 185-194 Wallace, C.S. and D.L. Dowe (1999a). Minimum Message Length and Kolmogorov Complexity, Computer Journal (special issue on Kolmogorov
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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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. Required knowledge Strong background in machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch
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analysis. Additionally, experience in healthcare informatics, user experience research, and a commitment to improving mental health services are highly desirable. Project funding Project based scholarship
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computational techniques can be combined with classical systems to improve performance, scalability, and solution quality for tasks such as: Similarity search and nearest-neighbour queries Graph and routing