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the knowledge and tools needed to regenerate the British countryside using cost-effective nature-based solutions. The power of ecosystems will be harnessed to provide broad societal benefits including
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Boreal forest recovery after clear-cut: We are looking for a highly motivated PhD student to collect data on and model hydro(geo)logy and greenhouse gas exchanges of boreal forests after
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at the interface between stochastic modelling, signal processing and data science. Ultimately, the project will develop key indices that can be used to assess the health of the soil ecosystem. Such indices
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of the assembly of these complex microbial communities using ecological theory and mathematical models. The questions we address are: (1) how does the microbial community change during cultivation
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develop novel approaches that integrate uncertainty estimation and confidence-aware predictions, enabling models not only to classify species but also to quantify their reliability. Such methods are crucial
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powerful framework for decentralised machine learning. FL enables multiple entities to collaboratively train a global machine learning model without sharing their private data, thus enhancing privacy
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testing and computational modelling. You'll become part of a diverse, multidisciplinary team that prioritises equity, diversity, and inclusion, gaining specialist expertise in hydrogen-material interactions
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-class facilities, enhancing their skills in materials characterisation, computational modelling, and experimental testing. These experiences will position the graduate as an innovator, ready to
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develop a comprehensive understanding of the wider aviation ecosystem. This holistic experience ensures graduates are prepared to lead and accelerate aviation decarbonisation efforts from various roles in