34 machine-learning-modeling-"https:"-"Computer-Vision-Center" Postdoctoral positions in France
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 4 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 | 6 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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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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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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models Foundation models represent a breakthrough in AI, as did the shift from traditional machine learning to deep learning. Numerous models become available in the field of Earth Observation and can be
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- 4 Additional Information Eligibility criteria • Experience in computer modeling and programming • Knowledge of associative learning at both the neurobiological and psychological levels • Experience
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quantitative and machine learning approaches ● Developing predictive models linking nuclear features to future cell fate ● Interacting with collaborators in imaging, computational biology, and developmental
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implement machine learning models dedicated to the prediction, interpretation, and quantitative analysis of Raman vibrational spectra, establishing explicit links between structure, local chemical environment