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algorithms for information gathering problems involving one or more rational autonomous agents. The objectives include: the development of learning algorithms; the study of their theoretical properties
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(statistical and/or computational); and, where appropriate, their implementation and experimental validation through simulation. The research will make use of techniques from online learning and multi-agent
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, Robust and Secure AI-Supported Development, and Resilient Distributed and Agentic AI. RESIST will drive world-class research in the intersection between AI and cybersecurity through a strong, stimulating
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of interactive experimental environments in which simulated autonomous agents cooperate with human participants; the collection of behavioral data to validate estimation and decision models; the assessment
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Verifiable AI, Runtime Security Assurance, Robust and Secure AI-Supported Development, and Resilient Distributed and Agentic AI. RESIST will drive world-class research in the intersection between AI and
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CDT in Developing National Capability for Materials 4.0, with the Henry Royce Institute. ‘Agent-based’ modelling (ABM) simulates large numbers of autonomous yet interacting entities and in Sheffield has
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reducing friction as they slide against each other. Established surface-active agents deliver outstanding performance, but there are concerns about their environmental impact. Manufacturers are now anxious
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, please contact Damien Freeman, Facility Manager - Centre for Maritime Simulations on D.Freeman@utas.edu.au or 0363249526. Please visit https://www.utas.edu.au/jobs/applying for our guide to applying and
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networks. Participation in national and internationally funded research projects. Contribution to advanced digital twin and agent-based simulation platforms. Opportunities for interdisciplinary collaboration
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Evolution. Please check our publications for more details: http://garciajulian.com [1] “Empirical Agent Based Models of Cooperation in Public Goods Games | Proceedings of the Fourteenth ACM Conference