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criteria - Emerging technologies modeling, - circuit simulation and design. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR5270-SYLGON-070/Default.aspx Work Location(s) Number
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models are deeply rooted in real-world biological data. The collaborative approach allows for the development of predictive models that bridge the gap between theory and experiment, with a focus on high
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the Biomechanics, Wood, and Bio-based Materials axis of LMGC (Laboratory of Mechanics and Civil Engineering, Montpeliier), where we carry out research (experimental and modeling) on the biophysical determinism
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researcher in data-driven modelling of cyber vulnerability risk within the defence domain (1.0 FTE). The Center for Cognitive Science & Artificial Intelligence in the Department of Intelligent Systems
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More Jobs from This Employer https://main.hercjobs.org/jobs/22121024/postdoctoral-research-associates-statistical-and-computational-modeling Return to Search Results
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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 Establish and validate a regional coupled ocean modeling framework at very
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 15 days ago
/Administrative Internal Number: 25565 POSTDOCTORAL ASSOCIATE, McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, to develop computational circuit models, analytical theory, and
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models, cell culture, immunologic assays Where to apply Website https://www.lih.lu/en/job/?value=JA/PDGMMI0326/MD/DII Requirements Research FieldOtherEducation LevelPhD or equivalent
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Post-doctoral position (M/F) for testing drought-based BEF relationships at CEFE Montpellier, France
) Carry-out additional simulations with the Phoreau model to test the effect of tree diversity on forests' resistance to droughts. ii) Analyse biodiversity-drought resistance relationships, across a
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for the analysis of hyperspectral imaging data applied to pictorial layers, based on coupling physical radiative transfer models (two-flux and four-flux approaches) with machine learning methods. The researcher will