439 machine-learning-"https:" "https:" "https:" "RAEGE Az" positions at Monash University
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This research project aims to address the critical need for privacy-enhancing techniques in machine learning (ML) applications, particularly in scenarios involving sensitive or confidential data
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Academic transcript Applications Close: Sunday 1 March 2026, 11:55pm AEDT Minimum entry requirements: https://www.monash.edu/admissions/entry-requirements/minimum Research webpages: https://www.monash.edu
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statement of interest Academic transcript Applications Close: Sunday 1 March 2026, 11:55pm AEDT Minimum entry requirements: https://www.monash.edu/admissions/entry-requirements/minimum Research webpages
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significant step forward in machine translation capabilities. However, "NMT systems have a steeper learning curve with respect to the amount of training data, resulting in worse quality in low-resource settings
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Adversarial Machine Learning (AML) is a technique to fool a machine learning model through malicious input. Due to its significance in many scenarios, including security, privacy, and health
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. Scientific Contribution Our group has strong publication record of 100+ first or senior author top-tier (ERA ranking A*/A) journals and technical conferences in the machine learning and medical AI field. His
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based on matched-filter statistics. Detecting the unknown relies on the development of complex algorithms at the forefront of statistics, machine learning, and data science. This multi-disciplinary
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. Required knowledge Python programming Machine learning background Image analysis Video analysis Audio analysis
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Project description: Nowadays, data-driven machine learning algorithms are well suited to solve real-world problems that require high-level prediction accuracy. However, it seems as if nothing beats
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The world is dynamic and in a constant state of flux, yet most machine learning models learn static models from a dataset that represents a single snapshot in time. My group's research is