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properties and processes, as well as how soil functions are affected by changes in environmental conditions, including climate change. Through research, environmental analysis and education, we contribute
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inherently spatio-temporal, i.e. physical processes around us evolve over both time and space, making spatio-temporal processes and data omnipresent in science and technology, with applications ranging from
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spoken English. Experience from prior studies on environmental politics, forest governance or Swedish policy processes and political culture is considered a merit. Interviews will be conducted with a short
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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of models of clouds and climate at Lund. A new climate model is being developed to represent cloud processes with more realism in the project. The post will involve global modeling with this new climate
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particular attention to how AI systems reshape organizational processes, decision-making practices, and service delivery. The project is situated within Swedish Center for Digital Innovation at the Department
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the analogue and the digital. A central focus lies in the meeting of materials – how different wood species, biocomposites, and hybrid materials react and interact when processed with both traditional and
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process models from the field of spatial statistics to model clustered patterns across the landscape, and develop methods for estimating plant population size and/or change. Qualifications: Requirements
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using remote sensing and bioacoustics to track forest conditions, to examining soil and microbial processes that support ecosystem resilience, to developing genetic strategies that improve tree
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processes with more realism in the project. The post will involve global modeling with this new climate model at Lund to investigate mechanisms for the effect from ice nucleating aerosols on clouds and