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millions of images of wildlife worldwide, offering unprecedented insights into ecosystems. However, analysing these datasets is a major challenge: AI systems excel at detecting common species but often fail
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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
of machine learning, uncertainty quantification, and Bayesian modelling. They will provide complementary expertise to bridge agentic AI with real-world impact. What We Are Looking from You Background in
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coordination problems, conflict resolution, and cooperative/competitive behaviour among cyber-physical actors/agents while optimizing for their local and global objectives such as minimizing generation cost
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Hazard Maps, Global Wildfire Information System (GWIS) and Global Drought Observatory. The DR would co-develop the research objectives. Some ideas to discuss include (1) incorporating predictive modelling
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investment, helping to create an energetic and vibrant academic community for our students and staff. To find out more about the Department of Engineering please click on the following link https
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investment, helping to create an energetic and vibrant academic community for our students and staff. To find out more about the Department of Engineering please click on the following link https
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investment, helping to create an energetic and vibrant academic community for our students and staff. To find out more about the Department of Engineering please click on the following link https
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-efficiency trade-offs, using automated configuration to find Pareto-optimal designs under real deployment constraints. 2) Build the distributed learning loop. Develop the learning and update mechanisms
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provide large and complex datasets. By applying advanced pattern recognition and clustering algorithms, the aim is to automatically detect coherent spatial domains. These domains represent regions with