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subsidiarity at the territorial scale," specifically through the "materials for energy storage" program. Using molecular modeling tools, the objective is to participate in the design of a single catalyst capable
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position is available to work on computational models of artificial evolution for aptamer design, expected to start between November 2025 and February 2026. Aptamers are often obtained through rounds of in
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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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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 11 days ago
structures and as there are strong occlusions. The 3D reconstruction of flowers has received relatively little study. An early model reconstructs static flowers using botanical priors [1] by building a shape
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 5 days ago
(Lille University Hospital) and with all members of the Numetab consortium. The following main tasks will be implemented: 1. Modeling of weight curves using scalar functional data [3] 2. Modeling
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(Choi et al., 2023; Pousse-Beltran et al., 2025) or high-resolution (Gannouni et al., 2025). The development of such novel AI models is supported by the introduction of public datasets (Yaqoob et al
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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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evolutionary methods. Their fundamental principle is not to work directly on a population of candidate solutions using operators, but to explicitlylearn a probability distribution that can model the regions
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of these reusable packaging using IoT sensors and deep learning techniques embedded in the sensors. During the preliminary work, neural network models were developed to perform simple tasks using accelerometer data