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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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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 13 hours 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
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biology OR virology A solid background in virology, transcriptomics and imaging Hands-on experience with experimental animal and/or iPSCs models Technical expertise in working in BSL-3 environment A
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BioEngineering Experience with fluidics devices, 3D printed devices, 3D cell culture models and bioinformatics/computational biology (e.g., R, Python) is an asset Team player Proficient knowledge in English is
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establishments (Université Côte d'Azur, CNRS, INRAE, INSERM ...), but also with the regiona economic players. With a presence in the fields of computational neuroscience and biology, data science and modeling
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Strong understanding of causality concepts, biases, randomized trials, and trial emulation (target trial emulation) Familiarity with standardized data models (e.g., OMOP-CDM) Genuine interest in applied