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PhD Scholarship - Energy-Efficient Decentralised Training Frameworks for Large-Scale AI Models on Geo-Distributed Infrastructure Job No.: 696617 Location: Clayton campus Employment Type: Full-time
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trustworthy decision support. This PhD project aims to develop next-generation Trustworthy Agentic AI frameworks that integrate multi-agent collaboration, large language models, retrieval-augmented generation
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datasets, electronic health record-style data, or simulated clinical AI-agent workflows. The exact scope can be refined based on the candidate’s background and available datasets. Possible PhD Contribution
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investigate how large language models, recommender systems, advertising systems, and data brokers may infer sensitive traits such as personality, emotional state, political inclination, social vulnerability
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This PhD project focuses on the design and evaluation of hybrid quantum–classical algorithms for large-scale data analytics and optimisation problems. The research will investigate how quantum
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As education systems increasingly adopt AI to support teaching and learning, the automation of assessment and feedback processes has emerged as a critical area of innovation. Large-scale learning
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are computationally expensive for modern large models. Incomplete Uncertainty Modeling: Most methods focus on single-modal data and fail to account for uncertainty arising from multi-view or multimodal interactions
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, and costs of running diverse applications in large-scale distributed systems. This project offers researchers and students a chance to explore cutting-edge concepts in AI-driven infrastructure
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This PhD project aims to mitigate the data scarcity of new NLP and Multimodal applications by developing novel active learning algorithms. In this project, the student will leverage large foundation