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that can be run. Emulating expensive processes could allow more data to be generated from better models, at lower cost. The central science question is: how can machine learning and evolutionary computation
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pilots to real-world systems. The overarching aim is to deliver a scalable approach, pairing shared “aggregator” models with household-specific “client” models that exchange knowledge while keeping data
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Productivity Index (RPI) using observed versus potential productivity modelled with machine learning (https://doi.org/10.1016/j.ecolind.2025.113208 ), this applied geospatial ecology project will study how
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monitoring and conservation applications, while Bristol offers advanced training in machine learning, spatiotemporal modelling and AI applications to animal behaviour. Together, they provide computational
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interest, narrowing the scope to natural or cultural sites, and integrating diverse remote sensing datasets. The supervisory team offers interdisciplinary expertise in geospatial analysis, machine learning
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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