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This PhD project is funded by a successful ARC Discovery Project grant: "Improving human reasoning with causal Bayesian networks: a user-centric, multimodal, interactive approach" and the successful
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We have several PhD opportunities available in areas such as Multimodal Large Language Models (MLLM) for human understanding, MLLM safety, and Generative AI. If you have published in top-tier
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the social, cognitive, and communicative skills needed to autonomously engage in meaningful, long-term human-robot interactions. Project overview: Social robots are designed to be competent partners that help
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learning, algorithms, and programming. Prior exposure to reinforcement learning or human-robot interaction is highly desirable, though motivated candidates with a strong grounding in AI/ML and willingness
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We have several PhD and Research Assistant (RA) opportunities available in areas such as Multimodal Large Language Models (MLLM) for human understanding, MLLM safety, and Generative AI. If you have
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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
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the automatic detection of scams, the onus is often pushed back to humans to detect. Gamification and awareness campaigns are regularly researched and implemented in workplaces to prevent people from being
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Ecological systems are dynamic and complex. Many ecosystems support human food production and in turn are impacted by human food production activity. This creates feedback loops between ecosystems
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This scholarship will provide a stipend allowance of $29,000 AUD per annum for up to 3.5 years, plus $4,000 travel allowance. If you are currently in Australia you are strongly encouraged to apply. If successful, you will join Dr. Roberto-Martinez Maldonado, Prof. Dragan Gasevic, and a strong...
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requires substantial human expertise. Conversely, in real-world scenarios and after just a few data samples, humans are able to quickly uncover the underlying pattern of apparently patternless data and to