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of Machine Learning (ML) models across large-scale distributed systems. Leveraging advanced AI and distributed computing strategies, this project focuses on deploying ML models on real-world distributed
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Monash Residential Services - Front Office, Admissions and Operations. The Program ensures that the graduates will gain experience through on-the-job learning and develop the ability to provide a range of
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hands-on technician with a strong technical foundation and the drive to learn. We’re looking for: Background in electrical, electronics, instrumentation, automotive, auto electrician or cabling trades
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and oral communication skills, including drafting of academic papers and grants High level computer skills with software skills such as Microsoft Office, SAS, SPSS If this sounds like you, we highly
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and Boulton, 1968; Wallace and Dowe, 1999a; Wallace, 2005) is a Bayesian information-theoretic principle in machine learning, statistics and data science. MML can be thought of in different ways - it
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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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networks that can be trained to do machine learning and AI tasks in a similar way to artificial neural networks. In this project you will develop machine learning theory that is consistent with the learning
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. Required knowledge Python programming Machine learning background Image analysis Video analysis Audio analysis
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independently and as part of an interdisciplinary and cross-cultural team, possess excellent organisational and communication skills, and work collegially with other staff. Advanced computer skills, including
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technologies will affect them. It is our anticipation that the work will commence with, in parallel, the survey for collecting the data and a comparison of machine learning methods on artificial pseudo-randomly