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We are excited to offer a fully funded PhD position at the Faculty of Engineering, Monash University (Australia). This project focuses on developing new algorithms to equip social robots with
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Cooperation is difficult whenever individual incentives conflict with a shared goal. This problem appears in human societies, biological systems and groups of artificial agents. It also lies behind
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and AI PhD candidate interested in agentic AI, supervised by Dr Teresa Wang and Dr Tongtong Wu at Monash University, jointly with Dr He Zhao and Dr, Dan Steinberg from CSIRO, Dr Yue Yang and David
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explored through touch. The RTD is the blind person's equivalent of the computer visual display. The second is genAI-powered conversational agents. These enable the blind user to control the RTD and obtain
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World models are becoming an important direction in artificial intelligence and robotics. Instead of responding only to what is currently observed, an intelligent agent can use an internal model
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hinder their adoption in real-world clinical practice, where explainability, trust, and accountability are essential. Recent developments in Agentic AI offer a promising paradigm shift. Rather than relying
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computation is necessary before taking an action. This PhD project will address these challenges by developing trustworthy and resource-adaptive VLA models. A central question is whether an embodied agent can
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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
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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
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that remain secure, privacy-preserving, explainable, and clinically reliable when exposed to adversarial attacks, prompt injection, poisoned data, privacy leakage, and unsafe autonomous AI-agent behaviour