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This research aims to design a sustainable framework for optimizing distributed computing systems to enhance performance while minimizing energy consumption. Existing scheduling algorithms often
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abdominal symptoms. Standalone clinical indicators like CA125 or traditional screening algorithms (e.g., ROMA) frequently produce ambiguous results or lack sufficient sensitivity for early detection
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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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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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human-centred embodied AI New methods for modelling human behaviour and interaction dynamics Multimodal temporal and predictive learning algorithms Experimental evaluation against strong contemporary
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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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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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learning, algorithms, and programming. Prior exposure to reinforcement learning or human-robot interaction is highly desirable, though motivated candidates with a strong grounding in AI/ML and willingness
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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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This Masters or PhD project aims to explain the uncertainty of Machine Learning (ML) predictions. To this effect, we must quantify uncertainty, devise algorithms that explain ML predictions and