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. The result will be a thesis in the field of interaction design, contributing to our understanding of experiencing the human body as play. For more info see http://exertiongameslab.org
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to work closely with other leading academics at Monash University, including Professor Carol Propper , A/Prof Terrence Cheng and Dr Danusha Jayawardana . As a candidate in the CHE Integrated PhD Program
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inference. PhD Program The project is based in the Centre for Health Economics, a large and active economics research group within the Monash Business School in Melbourne, Australia. As a candidate in the CHE
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analysis, contextual analysis, audio feature extraction, and machine learning models to identify and assess potentially dangerous content. Similarly, computer vision models are implemented to analyse images
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This project explores the development of digital tools that measure the carbon footprint and nutritional impact of meals to support sustainable eating. It aims to integrate environmental and nutritional data into a user-friendly platform, enabling consumers, restaurants, and policymakers to make...
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principles and safe work practices. High-level computer literacy and the ability to quickly learn and adapt to new systems. About Monash University At Monash , work feels different. There’s a sense of
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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 Discovery Project, this research aims to develop highly novel physics-informed deep learning methods...
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that occurs within these biological neural networks, so that these networks can be leveraged for AI applications. In addition, you will develop mathematical and computational neuroscience models
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Hoven (elisevandenhoven.com). See also our dedicated post about this on our website. For more information see http://exertiongameslab.org The result will be a thesis in the field of interaction design.
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anomalies in evolving graphs. In this research proposal, our aim is to explore the parallels of deep learning and anomaly detection in dynamic graphs. In particular we are interested to redesign deep neural