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"A picture is worth a thousands words"... or so the saying goes. How much information can we extract from an image of an insect on a flower? What species is the insect? What species is the flower? Where was the photograph taken? And at what time of the year? What time of the day? What was the...
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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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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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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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🎯 Research Vision The next generation of software engineering tools will move beyond autocomplete and static code generation toward autonomous, agentic systems — AI developers capable of planning
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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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settings. Candidates will also be expected to engage in a participatory research approach, involving blind and low vision end users as well as sector professionals References Cheng, W., Luo, Z., and Yin, Q
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Pro Vice-Chancellor (Research) Job No:Â 689607 Location: Clayton Campus Employment Type: Full-time, Fixed-term appointment Remuneration: A competitive remuneration package will apply Lead with vision
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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