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that are constructed in a way that is inspired by what we know about self-awareness circuits in the brain and the field of self-aware computing. The project will advanced state of the art AI for NLP or vision or both
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In collaboration with people from Monash materials engineering, neuroscience and biochemistry we are developing living AI networks where neurons in a dish are grown to form biological neural
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explore current techniques such as fine-tuning, model alignment, prompt engineering and Retrieval Augmented Generation (RAG) to improve reliability of generated recommendations for two cases of chronic
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healthcare, finance, environmental monitoring, and beyond. While recent advancements in foundation models have shown tremendous success in NLP and computer vision, the unique characteristics of time series
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their performance both empirically and through controlled user studies. Required knowledge Strong background in computer science in general Familiarity and understanding of basic principles underlying automated
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The detection of human activities is crucial for effective monitoring purposes. The challenge lies in accurately and promptly identifying various types of activities from videos and images captured in diverse, real-world environments. Both classical machine learning methods and deep learning...
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Motorola Solutions Leap Scholarships Industry Leaders Scholarship The Motorola Solutions Leap Scholarships are designed to support students pursuing a Bachelor of Paramedicine (Honours) or a Master of Specialist Paramedic Practice. These scholarships aim to help equip the next generation of...
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explore unconventional ideas, develop computer algorithms for data analysis, create new experimental approaches, and apply the technique in areas like biomedicine, materials science, and geology. My group
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the Monash Engineering program. Am I eligible? You must be one of the following: An International student You must meet the following criteria: A commencing student enrolled or intending to enrol in
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. Leveraging techniques such as federated learning, differential privacy, and secure multiparty computation, the goal is to enable collaborative ML tasks without compromising the privacy of individual data