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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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approach including empirical data analysis, experiments, and theoretical modelling to develop science-based management strategies for the restoration of woodland ecosystems. We will collect, and collate from
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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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terrestrial biodiversity and ecosystem services. However, critical gaps remain in understanding how the design, structure, and landscape context of solar parks influence ecological communities, and how habitat
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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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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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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