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early-career researcher in applied mathematics, physics, or engineering, with expertise in fluid mechanics1, non-Newtonian fluids, numerical methods, and scientific computing2. Background or strong
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months 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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contribute to various aspects of the project, such as: - developing new theoretical approaches to model electrode/electrolyte interfaces - performing molecular simulations, such as molecular dynamics
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on the modeling of tumor growth. Tumor growth is a complex phenomenon, influenced by numerous biological, metabolic and environmental factors. Morphological magnetic resonance imaging (MRI) is widely used to detect
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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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Paris, is one of France's top 5 general engineering schools. The mainspring of Télécom Paris is to train, imagine and undertake to design digital models, technologies and solutions for a society and
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insights into MCTP action and ER tubularization at PD, the post-doc will use molecular modeling and dynamics. - undertake simulations - present results in team meetings - present results as scientific
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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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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The activities of the PHABIO group focus on developing models and acquiring biological
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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