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community of staff are united by our purpose to inspire Australia’s future change-makers and create a better tomorrow. Work that matters Advance the frontier of AI by developing multi-agent systems capable
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://cavecore.eu/ Your Research Project (DC4) You will work at the intersection of machine learning, control theory, and autonomous multi-agent systems to develop hybrid learning-based control strategies
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behaviours of multi-agent systems in response to changing internal states and external environmental conditions. Both traditional model-based approaches and modern learning-based control techniques will be
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-enabled adaptation. The aim is to develop theoretically grounded yet practically deployable algorithms that allow multi-agent robotics to operate robustly in dynamic, uncertain, and interactive environments
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for Enhancing Decision-Making in Networks of Autonomous Agents”, which aims to develop a new methodological framework to improve decision-making in autonomous systems. The project combines high-frequency radio
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, abandonment, comments, peer interaction) Formalization of algorithms for orchestrating educational AI agents : Train RL and LLM agents and study multi-objective optimization (mastery, well-being stability) Work
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. AI-based performance assessment in early design, integrating rapid analysis tools, multi-criteria performance estimation, and surrogate modeling. Human–AI collaboration in design, including agent-based
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on a Conventional Imaging System for In Vitro Characterization of Contrast Agents. Sensors 22, 6543. https://doi.org/10.3390/s22176543 Nicolas-Boluda, A., Yang, Z., Guilbert, T., Fouassier, L., Carn, F
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correct, controllable, auditable, and SLA-compliant. The PhD work is grounded in the systems reality of deploying AI agents against real network platforms, where decisions about whether to execute
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., 2022. Calibrated Photoacoustic Spectrometer Based on a Conventional Imaging System for In Vitro Characterization of Contrast Agents. Sensors 22, 6543. https://doi.org/10.3390/s22176543 Nicolas-Boluda, A