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evolutionary and behavioural game theory, multi-agent reinforcement learning, agent-based simulation, or experiments with people and AI systems. Some students may develop new theory or algorithms; others may use
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, data processing pipelines, machine learning and generative AI applied to physical activity and sleep research. The position will contribute to the development of an AI-based behaviour change tool and
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estimation, confidence calibration, model routing, token selection, early exiting, adaptive visual processing, and efficient use of multiple foundation models. The work will combine algorithm development with
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. Selected methods can then be evaluated through real-world human-robot interaction using humanoid and mobile robotic platforms. Aim/outline The aim of this project is to develop multimodal world models
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This PhD project focuses on the design and evaluation of hybrid quantumโclassical algorithms for large-scale data analytics and optimisation problems. The research will investigate how quantum
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at prediction and pattern recognition tasks but still fails at very simple planning and decision-making problems. This project will develop predictive and prescriptive analytics algorithms that combine
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๐๐ง๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง ๐๐๐๐ก๐ง๐จ๐ฅ๐จ๐ ๐ฒ at Monash University and work closely with ๐๐ฅ๐ฎ๐ซ๐๐ฅ๐ข๐ฌ ๐๐๐ฌ๐๐๐ซ๐๐ก, the industry partner on this project. The project focuses on developing ๐๐ง๐๐ซ๐ ๐ฒ
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Christopher O'Neill ) Auditing Algorithms: Detecting and reducing discrimination in AI-Driven Decisions (Dr Charles Crabtree ) The changing nature of digital inequalities in an age of AI (Prof. Neil Selwyn
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automated recovery algorithms, improving system resilience. Research Areas for Masterโs and PhD Students AI-Enhanced Resource Forecasting and Optimization: Research Focus: Developing and testing ML algorithms
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experiments for months before the value of outputย yย is measured for some given inputย x. This creates an exciting challenge for AI researchers to develop smart algorithms that can find the optimal value of input