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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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intelligence, and multimodal learning. The main objective of this position is to develop novel generative AI methods for computer vision applications, with a particular focus on Diffusion Models and Vision
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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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The postdoctoral researcher will join the "Network Dynamics & Computations" team led by Srdjan Ostojic and develop research projects on modeling neural circuits and their role in behavior. The work will focus
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based on an individual-centered spatial analysis approach (geomatics) using AgentBased Modeling to model mobility and its evolution. The postdoctoral researcher will intervene in the second phase
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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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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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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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discrete black box combinatorial optimization problems (https://arxiv.org/abs/2510.01824 ). In this work, we parameterize a multivariate autoregressive generative model for generating solutions. By sampling