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approach. The project's overall framework encompasses the key facets of biodiversity (levels of biodiversity, biological groups and variability), the forces and processes that shape them, their spatio
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component—as low-carbon binders. Recent work by the team has shown that bubbles naturally concentrate around grain contacts and form liquid bridges there, enabling the binder to be deposited precisely where
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-stimulatory peptidoglycan fragments to shape the host immune system using in vitro organoids, gut-on-chip and in vivo models. The successful candidate should have a strong expertise in microbiology and
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unexpected phenotypes. Internal Application form(s) needed postdoc_CG_2026.pdf English (488.45 KB - PDF) Download Additional Information Selection process Applications should be sent to : Dr Valentin Greigert
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The candidate should have a PhD and expertise in Plasmonics, Raman spectroscopy and SERS. Research FieldChemistryPhysics Internal Application form(s) needed Offre emploi-202608-31.pdf English (53 KB - PDF
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/chercheureuse-postdoctorale-biochimie-veg… Requirements Research FieldBiological sciences » BotanyEducation LevelPhD or equivalent Internal Application form(s) needed
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their reactivity with dinitrogen, which can be reduced and then functionalized to form ammonia or amines. The project will involve synthesizing actinide complexes with ligands developed by a team of doctoral and
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Application form(s) needed Postdoctoral Researcher WallMat.pdf English (942.4 KB - PDF) Download Additional Information Benefits So join us and enjoy these many benefits: Generous vacation time On-site dining
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-career scientist to develop cutting-edge machine learning approaches for understanding and designing pathogen antigens. This is a unique opportunity to help shape a new research program at the intersection
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 2 months ago
simulation, shape/topology and system-level design optimization applied to deformable systems — advanced level Data-driven design and modelling approaches (machine learning applied to physical/mechanical