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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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defence algorithms are remain a challenge in the research community. Furthermore, most existing AML algorithms can only apply to Euclidean space. How to extend existing AML algorithms to non-Euclidean and
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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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achieve what neither a human being nor a machine can achieve on their own. The aim of this research is to develop cutting-edge Human-in-the-Loop Machine Learning algorithms that are able to avoid bias
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(Honours) and Bachelor of Commerce double degree to inspire female students to develop their leadership skills. Total scholarship value Up to $20,000 Number offered One See details Maria Volodin IFM Industry
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stakeholders Well-developed problem-solving skills with the ability to identify and recommend solutions to challenging issues Monash benefits include: Hybrid and flexible work arrangements available - Work from
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Intelligence This project works with leading researchers in the Faculty of Pharmacy to develop new artificial intelligence technologies to aide discovery of drugs to treat pharmacoresistant epilepsy. Required
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will study the development, adoption, and implications of digital technology and insurance—such as tools for capturing individualised data about behavioural risk factors and automating enforcement
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feasibility, and to facilitate the rapid translation of study findings into registry practice and health data environments. Project goals: The aim of the project is to develop cutting-edge AI algorithms
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will provide new models and algorithms for energy-transport integration, advancing the knowledge of mitigation strategies for sustainable urban development. #sustainability PhD student role description