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- Université Grenoble Alpes, laboratoire TIMC, équipe GMCAO
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to specialize the memory management of several applications, including virtual machines. Running memory management policies in user space opens up new opportunities, particularly the integration of AI models
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influence parameters used in seismic modeling according to the Eurocodes. DYNATERRE adopts a multi-scale approach—from raw materials to the global behavior of the building. It is based on collaboration
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for biomarkers in 7T images. - Development of artificial intelligence algorithms and models for the processing and analysis of MRI images/spectra, focusing on the detection of tumor tissue and the quantification
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Infrastructure? No Offer Description The postdoctoral researcher will contribute to the ANR-funded Pi-CANTHERM project, which aims to design, model, and predict the performance of new n‑type organic thermoelectric
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and physics, Materials Mechanics, Geotechnics, and Civil Engineering Proven skills in laboratory and/or field experimentation Potential aptitude in numerical modeling Ability to work in a team and in
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, immunoblotting) and of phenotypic characterization of animal models Where to apply E-mail laurent.lecam@inserm.fr Requirements Research FieldBiological sciences » BiologyEducation LevelPhD or equivalent Skills
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identical conditions, using Trichloroethylene (TCE) taken as a model molecule. In addition to physical measures, a methodology based on the analysis, in solution, of TCE and its degradation by-products will
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obtained from ex vivo robotic tests will inform multiscale numerical models of tissue mechanotransduction, calibrated using in vivo murine gait analysis and behavioral tracking provided by IRMB
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results and absence of an explanatory condition) taken as a model of medically unexplained symptoms in adulthood. Analyses will be conducted using data from the French CONSTANCES population-based cohort
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Université Grenoble Alpes, laboratoire TIMC, équipe GMCAO | Grenoble, Rhone Alpes | France | about 1 hour ago
multi-expert segmentation databases. The postdoctoral fellow will focus on integrating segmentation variability into deep learning models, with the goal of assessing prediction reliability and enabling