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background in AI/ML, data science, or signal processing Interest in music informatics, emotion modelling, or multimodal AI Ability to implement and evaluate machine learning models independently Commitment
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completion); Applied for admission to a PhD program at an Australian university or be a student enrolled in their first 12 months of study in a PhD program at an Australian University; A university supervisor
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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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and innovative scientific discovery. We are seeking a Veterinarian to contribute to a comprehensive program of veterinary care and research support across a diverse range of species. Working within a
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Master of Engineering pathway program. Up to 10 scholarships valued at up to $30,000 are available each year. Applications No application required Total scholarship value Up to $30,000 Number offered Ten
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Goal Recognition is the task of inferring the goal of an agent from their action logs. Goal Recognition assumes these logs are collected by an independent process that is not controlled by the observer. Active Goal Recognition extends Goal Recognition by also assigning the data collection task...
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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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process natural language problem descriptions and translate them into executable code. This research seeks to streamline workflows across diverse domains, from software engineering to data engineering
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Current reseach is in the areas of: Development of biomimetic structures as ultrasound contrast agents Deep tissue imaging using photoacoustic contrast agents All optical photoacoustic sensors for tomagraphic imaging in tissue Neural network correction of distortions in acoustic transducers web...
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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