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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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the spatial distribution of woodburning emissions. Integrate observations into inversion modelling to refine regional and national emission inventories. Model the impact of woodburning on UK air quality and
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data. This position offers an exciting opportunity to work at the intersection of cancer biology, spatial multi-omics, and artificial intelligence. More information: https://digitalhealth.tu-dresden.de
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tasks. Key Competencies Integrates GIS, remote sensing, and environmental data for spatial modelling. Proficient in or able to learn ecosystem service software (e.g., InVEST, ARIES). Strong analytical
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, developing spatial statistical models, and translating results into actionable insights for policy and adaptation. The strength of the project lies in its interdisciplinarity, combining atmospheric science
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A crystal in space represents a distinct state of matter, with spatial periodicity in its lattice structure underpinning its band structure and optical properties. This project concerns time
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data efficiency. Decision Making/Autonomy (10%) – Lead architectural and data design decisions; prioritize experiments aligned with program milestones; evaluate trade-offs between model accuracy and
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editing, mouse modeling, iPSC disease modelling, cell sorting, live cell imaging, spatial and single cell omics modalities, and advanced image processing and analysis, amongst a variety of other specific
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uses cutting-edge techniques including single-cell and spatial transcriptomics, proteomics, super-resolution microscopy, in vivo tracking, mouse models, and human patient tissues and iPS-derived cells
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with ecosystem service models and spatial datasets. Key Competencies Strong programming skills (Python/R/JavaScript) for tool and interface development. Ability to implement or learn GeoTOPSIS/VectorMCDA