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(estimation filter) methods, while preserving the convex character of the optimization problems to be solved. An alternative path to consider will be the use of convex relaxations. As the expected contributions
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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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. Desirable Criteria Experience implementing machine learning or deep learning models (e.g., neural networks, probabilistic learning methods). Knowledge of state estimation techniques, such as Kalman filters
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systematically improve the quality of production processes and final products. Continuous improvement must be implemented as a managed and ongoing system supported by modern quality management methods and computer
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estimation method for the hazard function within the framework of cure models. This method aims to provide flexible, data-adaptive estimators with optimal statistical properties (optimal convergence rates and
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of experience: Data collection or data management Required License No If yes, what is the required licensure/certification? NA Required Computer Applications: Microsoft Excel, Microsoft Word Required Other
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projects as well as third cycle courses, seminars and conferences. You will perform standard physics-based methods to estimate absolute and relative binding free energy. Based on these, you will develop
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guidance, navigation, and control (GNC) systems. The successful candidate will develop and validate Bayesian and non-Gaussian estimation algorithms, data assimilation methods, and tracking frameworks
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 3 days ago
Website https://jobs.inria.fr/public/classic/en/offres/2026-09928 Requirements Skills/Qualifications Profile: - The candidate is completing a Master's or engineering’s degree in Computer Vision, Electrical
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methods, hpc computer programming and mechanics of structures. The candidate's experience and knowledge in the tasks to be performed in the project will be valued at 15%, in particular in numerical methods