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healthcare application needs to analyze sensitive patient data across distributed nodes. Researchers and students can explore privacy-preserving algorithms and technologies like federated learning and zero
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This project aims to develop robust algorithms capable of identifying and analyzing fingertips extracted from both static images and video footage. Machine learning techniques, particularly computer
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experiments for months before the value of output y is measured for some given input x. This creates an exciting challenge for AI researchers to develop smart algorithms that can find the optimal value of input
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queries, and automating data transformations. By combining advancements in natural language understanding, algorithm synthesis, and debugging, the proposed framework will enable developers to efficiently
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publicly available datasets; 3) Proposing algorithms aimed at improving the accuracy of human activity detection; 4) Implementing these algorithms, evaluating their performance empirically, and comparing
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the headspace website. Possible approaches to addressing this challenge might include: Developing algorithms to identify patterns and preferences based on service users’ previous content engagement
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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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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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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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. 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