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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 the social, cognitive, and communicative skills needed to autonomously engage in meaningful, long-term...
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species' distributions. This project harnesses research in ecological and agent-based modelling, machine learning, and AI to increase the predictive power of models of species’ distribution shifts via “data
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career trajectories. The goal is to design better incentives for scientists to produce their best work. Our research group studies how groups of agents can learn to cooperate. Most of our research focuses
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The Multi-Agent Path Finding (MAPF) is a combinatorial problem in which agents must find a path from a start to a goal location without colliding with each other. The optimisation group at Monash is
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The multi-agent path finding problem (MAPF) asks us to find a collision-free plan for a team of moving agents. Such problems appear in many application settings (including robotics, logistics
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– an essential component of a conversational agent that supports blind people in achieving data literacy. In this project, candidates will use a new generation dynamic tactile display as a modality in
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Login Recently added Indigenous (Energy) Explainability of Reinforcement Learning Policies for Human-Robot Interaction Decision AI for biodiversity Development of a GIS-Based Model for Active Citizenry
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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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PhD student(s) will join a vibrant team of postdocs, academics, and up to four PhD students working collaboratively across modelling, qualitative fieldwork, and optimisation techniques. PhD Research