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conditions. However, current global climate models (GCMs) lack the spatial resolution to capture these processes, while high-resolution regional models remain too computationally expensive for large ensemble
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spatial accuracy of approximately 5 nm and temporal accuracy of 2 to 5 ms in cell cultures on coverslips. The aim of this project is to achieve the same performance in depth in biological tissues
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intervention systems; digital healthcare systems; real-time physiological monitoring technologies; multimodal data integration and analysis for imaging and sensing. Human Centered Models and Systems: human stem
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and mitochondrial biology: primary human fibroblasts, mitochondrial bioenergetics, import assays, respiratory chain phenotyping, in vivo disease models. Multi‑omics: single‑cell and bulk transcriptomics
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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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disease ⎯ Establish and optimize 3D culture models to explore endothelial–epithelial cross-talk ⎯ Perform imaging and spatial transcriptomics; contribute to data analysis & interpretation ⎯ Write and
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available1Company/InstituteCentre d'études spatiales de la biosphèreCountryFranceCityTOULOUSEGeofield Contact City TOULOUSE Website https://www.cesbio.cnrs.fr STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn
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PhD researcher in AI:GENOMIX, you will contribute to next-generation models that rethink polygenic prediction and support the future of precision medicine. AI:X is an ambitious initiative at Aalborg
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biochemical models, data assimilation, spatial analysis and GIS approaches. • Programing skills (e.g. R or Python) for data manipulation and visualisation, and to perform statistical analysis (e.g. mixed models
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group Physical Geography, which has a sound reputation in the research domains of landslides, “human impact” on geosystems and spatial modeling of geomorphic processes as well as in the applied research