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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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the social and environmental implications of proximity models, combining spatial, mobility and socioeconomic data to develop indicators and tools for the toolkit. The candidate will also assume coordination
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, using humanized in vitro models and chimeric AD mice. The candidate will work closely with experts in bioinformatics & spatial omics. It is a full-time position, starting the 1st January 2026. Additional
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encompasses various in silico modeling techniques to cover the multiple spatial scales, i.e. from molecular detailed to multicellular high-level regulatory networks, up to tissue level finite element models
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oriented towards sustainable mobility (superblocks, cycle lanes, low emission zones and other proximity policies). The analysis will combine electoral data at a very disaggregated scale with spatial