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the spatial and temporal transferability of the models. • Projection of the relationships obtained in future climate scenarios using ADAMONT projections to assess how gravitational crisis episodes will evolve
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leverage large-scale AI models to: integrate heterogeneous EO data sources, such as satellite, aerial and in-situ data, across spatial and temporal scales; enable zero-shot or few-shot learning for rapid
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for establishing a research group dedicated to quantitatively assessing system-level implementation and market potentials of innovative climate solutions. This includes modeling climate solutions spatial and socio
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theories and models. This project aims to develop new insights into how bedrock incision processes interact with geological and climatic factors (i.e. spatially variable uplift, shield building, mega
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and structures in biomedical image data. Additionally, the candidate will analyze spatial transcriptomic data to evaluate spatial patterns of genes in tissues. They will primarily work on projects
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incentives, behavioural drivers, and land-use decisions at the meso-scale (e.g. river basins or regions), using clear case studies. You develop modelling approaches that bridge economic analysis and spatial
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cities in implementing effective climate action strategies through spatial planning. The appointee will be responsible for leading the modelling work on this project and coordinating the research and
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this goal. However, the computational expense of these models limits their use for generating forecasts, constraining the spatial resolution, level of physical complexity, and number of ensemble members
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the intersection of mucosal immunology, cancer, and spatial transcriptomics, with the ultimate goal to understand and harness immune responses in human diseases. The Engblom and Villablanca labs form a tight-knit
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developing approaches to leverage spatial data to better understand evolutionary histories. More information about the lab and their work can be found by visiting https://federlab.github.io/ About the