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The primary objective of this project is to enhance Large Language Models (LLMs) by incorporating software knowledge documentation. Our approach involves utilizing existing LLMs and refining them
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Skip to main content Main Menu - Primary Home Projects Supervisors Expression of Interest Contact Automated software testing and debugging with/without LLMs Primary supervisor Yongqiang Tian Research
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Lead & innovate the Future: Three Lecturer Positions in AI, IoT & Software Systems Job No.: 688354 Location: Clayton campus Employment Type: Full-time Duration: 3-year fixed-term appointment
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🎯 Research Vision The next generation of software engineering tools will move beyond autocomplete and static code generation toward autonomous, agentic systems — AI developers capable of planning
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Networked digital diagnostic, monitoring and patient treatment tools permeate medical practice. A plethora of telemedicine, national and other eHealth records, injury assessment, patient-specific
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plants they visit and pollinate. Bayesian networks (BNs), and other probabilistic graphical models, can provide a visual representation of the underlying structure of a complex system by representing
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resilience. Deep understanding and implementation of advanced Kubernetes networking architectures, utilising CNI plugins, ingress controllers, and service meshes. Operating a GitOps-driven platform, enabling
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generalization [3]. The recent work shows that disentangled representations can be defined from a causal perspective [1]. This is also relevant to causal representation learning, which aims for robustness and
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to concentrate on my academics and professional development. My professional path has been greatly influenced by the opportunities it has created for skill development, including networking events and involvement
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Aim/outline Graphs or networks are effective tools to representing a variety of data in different domains. In the biological domain, chemical compounds can be represented as networks, with atoms as