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systems capable of selecting and combining models of different complexity, in order to better represent groundwater dynamics and improve large-scale predictions under climate change. Objective — The PhD
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are planned for participation in several international conferences). Expected skills Knowledge and technical skills: • Master's degree (or engineering degree) in Evolutionary Biology, Ecological Modeling
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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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condensed matter physics • Ability to learn and develop skills in analytical computation, theoretical modelling and numerical simulations, in particular the numerical solution of partial differential
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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
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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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centers [2,3,4]. This new paradigm addresses critical challenges such as latency, bandwidth, and energy efficiency, but also introduces new complexities in resource allocation, model deployment, and energy
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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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FieldTechnologyYears of Research ExperienceNone Additional Information Eligibility criteria In order to select the best HEA formulations, the aim will be to recruit a candidate capable of using thermodynamic modeling
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, optimal perturbations, resolvent methods) of Görtler vortices, identifying dominant modes and regions amenable to control. -Objective 2: Reduced-order modelling via autoencoders and discovery of explicit