72 structures "https:" "https:" "https:" "https:" "https:" Postdoctoral research jobs at CNRS
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, energy, transportation, and construction. The laboratory focuses on describing the relationships between processing, microstructure, and properties, using both experimental and modeling/simulation
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the first objective is to extend these findings to silicon solar cells. Two emerging concepts will be investigated and compared: (1) Multi-resonant absorption induced by perfectly ordered structures
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the boundary layer and the turbines, but also at the center. This should shed some new light on the dissipative structures and extreme events possibly responsible for the large scale evolution of the flow
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The postdoctoral researcher will participate in the construction and
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, and the introduction of the 2DEG to gauge the magnitude of the acoustoelectric effect, completed by numerical simulations. The acoustoelectric active structure will then be integrated in photonic
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, MultiFab, TRI-Genotoul). The project is embedded in a dynamic regional and national collaborative ecosystem focused on organ-on-chip technologies and personalized medicine. Where to apply Website https
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technology. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR5237-HIEPHA-008/Candidater.aspx Requirements Research FieldChemistryEducation LevelPhD or equivalent Research FieldPhysicsEducation LevelPhD
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, to maximize science return while constraining resources usage. - Support the structuring and drafting of relevant reports and deliverables - Engage with external and internal stakeholders at different levels
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charge/discharge cycles. The aim is to provide a unique tool to better understand the structure and evolution of interfaces/interphases in batteries, and thus, guide the design of more efficient and
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the flexibility and power of NNs with the ability of LMMs to robustly learn from structured and noisy (non i.i.d.) data, applying them on the prediction of both plants and human phenotypes. These models will