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in colorectal cancer. You will be responsible for developing spatially-resolved models of metastatic outgrowth in the liver which account for interactions between stromal, immune and tumour cells. You
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strategic programme. Through multiomic and spatial biology exploration of temporally distinct samples from clinical trials and advanced biological models, an international consortium of leading colorectal
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automating geospatial workflows. Proficiency with GIS tools (e.g., QGIS, ArcGIS) for spatial analysis and visualization. Experience developing statistical or machine learning models to estimate fuel moisture
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our lab including single-cell transcriptomic analysis and cellular growth quantification in the plant model organism Oryza Sativa (rice) and beyond. The candidate will work here mainly as a cell and
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targets will then be investigated in obese animal models to dissect the underlying cellular mechanisms and test potential compounds that modulate microglial pathways, with the ultimate goal of developing
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spatial network metrics. Strong quantitative skills, with experience in clustering analysis, data visualization, and statistical modelling. A research background in health-related topics. Demonstrated
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networks, to name but a few. The development of these systems requires realistic modeling of these propagation channels. The main difficulty lies in measuring this multi-dimensional propagation channel (time
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analyze and interpret multi-omic data to identify spatial patterns, cellular neighborhoods, and gene programs associated with drug resistance. Develop predictive models to infer tumor evolution and
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4 Oct 2025 Job Information Organisation/Company CNRS Department Centre d'études spatiales de la biosphère Research Field Environmental science Biological sciences Geosciences Researcher Profile
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hampered by uncertainties about viable business models and reward structures. Your job This postdoctoral position is part of ReGeNL , a large national inter- and transdisciplinary research programme aimed