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. With the widespread adoption of ML algorithms for data analysis and decision-making, preserving the privacy of individuals' data has become a paramount concern. The project focuses on exploring
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This PhD project is part of a larger project that aims to explain the uncertainty of Machine Learning (ML) predictions. To this effect, we must quantify uncertainty, devise algorithms that explain
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strategies using a meta-heuristic technique (e.g., genetic programming) to generate a large number of candidate solutions, and search for the ones that are optimal with respect to a number of objectives. The
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This project focuses on developing algorithms capable of automatically identifying and categorizing mobile ringtones. This involves leveraging machine learning techniques to analyze audio signals
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will design quantum-safe threshold encryption and/or authentication algorithms. The expected outcome is the design of methods, techniques and their software prototype to implement quantum-safe threshold
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at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need
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, pp270-283 Wallace, C.S. and D.L. Dowe (2000). MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions, Statistics and Computing, Vol. 10, No. 1, Jan. 2000, pp73-83.
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retention, models of care and healthy populations. These roles involve collaborating with a distributed team of rural health researchers to grow research capacity, attract strategic funding and deliver
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We are excited to offer a fully funded PhD position at the Faculty of Engineering, Monash University (Australia). This project focuses on developing new algorithms to equip social robots with
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learning approaches to enable multi-site collaboration while preserving patient privacy. This ensures more generalized and reliable reconstruction models that can adapt to diverse data distributions