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of bird genomes, working on some of the best-studied avian models (Mauritius parakeet, ringneck parakeet, red-crowned parakeet, and orange-bellied parrot). The PhD candidate will study data from
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, are crucial for modelling volcanic processes and are vital for understanding the transitions. Geophysical monitoring provides essential information to constrain these parameters and inform decision
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models. The project’s key objectives are to: 1) Identify critical indicators relating to ecosystem health and resilience; 2) Incorporate indicators into DBN models to simulate how ecosystems respond
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impacts of barrier winds and tip jets in current and future climates via time-slice comparisons from state-of-the-art climate model simulations. Training You will use observations from a series of Norwegian
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intensifying upper ocean mixing processes. However, major gaps in our understanding remain due to challenges in observing and modelling the Arctic Ocean. Research Methodology The aim of this project is to
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1-dimensional ocean-ice model. You will identify any long-term changes in ocean CO2 uptake along the Antarctic Peninsula using data from Rothera, SOCAT (www.socat.info) and mapped CO2 products, while
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extending the existing VL dataset. Design and evaluate DL models capable of classifying marine litter types using multispectral data, with a focus on achieving robustness to varying spectral channel
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model to resolve wheat planting strategies that can be used to safeguard biodiversity and suppress rust outbreaks. Training You will be embedded in the international and multidisciplinary Saunders Lab
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; biogeochemistry and fisheries modelling). The PhD candidate will acquire and/or strengthen their understanding of interdisciplinary working, modelling and statistics applied to economics, trade-off analysis skills
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automated feeders), and analyse microbiome composition using 16S rRNA and whole-genome sequencing. Statistical modelling will test for links between microbes and host development and fitness.� PROJECT