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Field
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. The objectives are : - Develop anisotropic internals for RPBs - Experimentally study the performance of the RPB with these internals, in particular the material transfer coefficient. - To model the hydrodynamic
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 4 days ago
growth phenotypes within a population of bacterial cells. So far, the data obtained from these technological breakthroughs have been exploited only in part. In particular, appropriate mathematical models
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for a full-time (100%) senior researcher to establish next-generation humanized models for studying peripheral neuroimmunity in Parkinson’s disease The Laboratories for Gut-Immune-Brain Axis Research
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The Department of Agroecology at Aarhus University, Denmark, is offering a postdoctoral position in crop modelling in climate adaptation and resilience, starting 01-12-2025 or as soon as possible
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-generation humanized models for studying peripheral neuroimmunity in Parkinson’s disease The Laboratories for Gut-Immune-Brain Axis Research (GIBA, Prof. Seppe De Schepper) and the Microglia and Inflammation
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far from straightforward. In particular, estimates of transfer rates based on documented cases are both biased and approximate. This project aims to develop explicit phylogenetic models of TE evolution
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for Molecular Neurology are recruiting a postdoctoral researcher to establish next-generation humanized and engraftment models for studying peripheral neuroimmune mechanisms driving Parkinson’s disease. Building
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research in several areas. Learning activities will focus on: The development and characterization of animal models and/or microphysiological systems for viral agents. Emphasis is placed on determining
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), whose objective is to extend the HLA-Epicheck model, originally developed within the framework of a PhD thesis, and to implement new deep learning approaches to assess donor–recipient compatibility in
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urgent to address the sustainability of database queries. The GREENSQL project tackles this challenge by combining reproducible measurements with advanced AI models to estimate, validate, and optimize