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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 6 days ago
) Optimization and parameter identification methods Data-driven modeling and machine learning Physics-Informed Learning (or hybrid modeling approaches) Handling and analysis of large-scale datasets (e.g., mobility
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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 | 8 days ago
., Data-driven Flower Petal Modeling with Botany Priors, CVPR 2014. 2. Q. Zheng et al., 4D Reconstruction of Blooming Flowers, CGF 2017. 3. S. Ghrer et al., Learning to Infer Parameterized Representations
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structure calculations, vibronic property simulations, and analyzing surface adsorption phenomena. Knowledge of machine learning potentials (e.g., GAP, ACE) or reactive force fields is a plus, as fallback
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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
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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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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
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 1 day ago
learning and have strong mathematical skills. Knowledge of bandit models is a plus. LanguagesFRENCHLevelBasic LanguagesENGLISHLevelGood Additional Information Benefits Subsidized meals Partial reimbursement
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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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new insights into the phenomena observed and enrich the databases required for deep learning methods. The neural networks currently being developed at LISTIC to detect and segment areas of movement in