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Field
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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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, sociological, economic and web data this project will apply machine learning to build a recommendation engine that can provide life advice and predictions to individuals based on their current life situation and
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With success stories ranging from speech recognition to self-driving cars, machine learning (ML) has been one of the most impactful areas of computer science. ML’s versatility stems from the wealth
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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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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
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On-device machine learning (ML) is rapidly gaining popularity on mobile devices. Mobile developers can use on-device ML to enable ML features at users’ mobile devices, such as face recognition
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the following knowledge and/or experience are highly preferred: Computer Vision, Signal Processing, Machine Learning knowledge and/or; Experience Industry knowledge and/or; A track record of published
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Machine learning has recently made significant progress for medical imaging applications including image segmentation, enhancement, and reconstruction. Funded as an Australian Research Council
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The PhD candidate will gain intensive knowledge in innovative processing protocols for chemical sensing and to develop data acquisition system with the Machine Learning (ML) and/or Deep Learning (DL