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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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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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an independent impact assessment of potential climate interventions in the Arctic marine environment through laboratory experiments and computer modelling. The team will develop physical, climate and ecosystem
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of areas suitable for persistence of healthy, accumulating peat (e.g., Ritson et al., 2024) under projected climate change scenarios. However, these models often fail to consider how these ecosystems might
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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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has galvanised global action, through initiatives such as the UN’s Decade on Ecosystem Restoration2, to protect what remains and restore what was lost. Yet, restoration of coastal wetlands at scale is
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across the member countries and facilitating collaborative engagement with the task and beyond. BACKGROUND AND APPROACH Land underpins the delivery of a wide range of ecosystem services beyond
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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