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Associate Professor - Electrical and Computer Systems Engineering Job No.: 678997 Location: Clayton campus Employment Type: Full-time Duration: Continuing appointment Remuneration: $175,487
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This project aims to employ advanced machine learning techniques to analyse text, audio, images, and videos for signs of harmful behaviour. Natural language processing algorithms are utilized
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Levin Kuhlmann Research area Machine Learning We are seeking a highly motivated and innovative PhD student interested in exploring the opportunities for using AI to enhance personalisation of services and
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. That’s why we champion an inclusive and respectful workplace culture where everyone is supported to succeed. Learn more about Monash . Our Finance Operations team is responsible for the management
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seeking a leading researcher to advance a program in Applied Clinical Data Science, Machine Learning and AI. This role offers the chance to lead high-impact projects, mentor emerging researchers, and
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. Application of artificial intelligence/machine learning to the big data from genetics and omics is well recognized in healthcare, however, its application to the data reported everyday as part of the clinical
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in diverse, real-world environments. Both classical machine learning methods and deep learning techniques can be employed to tackle this task. This project aims to achieve several objectives: 1
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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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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