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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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their performance evaluated in terms of classification accuracy, computational speed, and overall usability. Required knowledge Deep learning (CNNs, Transformers) and computer vision Knowledge distillation for model
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. Required knowledge Strong background in machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch
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. This work combines computational modelling and simulation with biological experiments that are analysed using cutting-edge computer vision techniques. We collaborate closely with Macquarie University where
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accepted by the intended users due to their limited capabilities to sustain long-term interactions. In this project we propose to develop compositional vision-language models for social robots, enabling them
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-model fusion”. The successful candidate will develop near-real-time integration between emerging environmental and ecological AI-driven data sources (e.g., automated acoustic and machine vision species
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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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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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Background and Motivation Modern deep learning models have achieved remarkable success in computer vision and natural language processing. However, they typically produce overconfident predictions
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infancy is difficult. This PhD program aims at using state-of-the art multi-channel near infrared spectroscopy (NIRS) to assess the functional brain response of infants born preterm in long-term follow-up