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Methods Tropical forest soils are crucial to the global carbon cycle, yet increasing wildfire, land-use change, and climate warming may cause large carbon emissions. This PhD will investigate (1) how soil
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predictive performance, computational efficiency, and spatial resolution through algorithm optimisation, tuning, and refined covariates. Assess trade-offs between spatial resolution and other performance
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, or applying consistent correction models across inter- and intra-satellite interactions. The use of multi-objective optimisation will enable systematic exploration of trade-offs between different classes
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Methods Marine bivalves such as mussels and oysters are vital for UK coastal ecosystems and support multi-million-pound aquaculture industries. However, their survival and performance are increasingly
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in Participating Departments Becoming a part of Exeter’s postgraduate research (PGR) community means you will have access to leading research facilities and industry connections. You’ll play a pivotal
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, machine-learning tools, and Lagrangian transport modelling. You will be based at the British Antarctic Survey and work closely with experts at the University of Leeds and Exeter, who provide cutting-edge
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-tuning only a small set of low-rank matrices for each agent role, drastically reducing GPU memory and training time while preserving the model's pre-trained knowledge. The primary outcome of this research
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. The ML will use 500,000 fundus images from open-source and customised retinopathy datasets. We will compare retinopathy grading accuracy by NHS clinician vs ML algorithm. This will build on Exeter’s
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existing models of reliability, performance, and safety. Similarly, in dynamic crowd scenarios, assumptions about orderly movement can break down due to panic or unexpected human behaviour, leading
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that challenges existing models of reliability, performance, and safety. Similarly, in dynamic crowd scenarios, assumptions about orderly movement can break down due to panic or unexpected human behaviour, leading