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Field
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, advanced co-culture organoid assays, and in vivo models to decode the mechanisms underlying CAF-driven CRC evolution. Access to single cell RNA sequencing and spatial transcriptomics data from active
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terrestrial gravimetry (measurements, processing, modelling) is an advantage - Understanding of geodetic data analysis and geophysical processes is an asset - Basis understanding of quantum physics is a plus
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 21 days ago
for careers in both academia and industry. Granular materials exhibit complex and distinctive behaviours arising from the disordered spatial arrangement of microscopic solid particles (grains). These materials
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as necessary. Analyze data to assess prevalence of lead exposure sources, associations with key outcomes, and spatial trends. Support or lead mixed-methods analysis combining survey data with
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involves working with commercial fishing scientific data compilation, analysis, modeling, and visualization, and integration of local fishermen’s ecological knowledge, and available socioeconomics scientific
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October 2025 Apply now The Department of Earth Sciences is looking for a highly motivated postdoctoral candidate with a background in numerical modeling (DEM, FEM, BEM). Your job You will work on the
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. Specifically, the project will: Address a fundamental knowledge gap about how chromatin 3D organization determines transcription at a global scale in an explicitly defined neuronal development model. Integrate
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runoff under climate change, and Analyze changes in marine ecological (phytoplankton) communities during and after freshwater extremes (low-salinity pulses) at multiple spatial and temporal scales
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resistance. This work will integrate spatial transcriptomics, advanced co-culture organoid assays, and in vivo models to decode the mechanisms underlying CAF-driven CRC evolution. Access to single cell RNA
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-culture organoid assays, and in vivo models to decode the mechanisms underlying CAF-driven CRC evolution. Access to single cell RNA sequencing and spatial transcriptomics data from active clinical trials