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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | 5 days ago
. This initiative addresses a critical challenge in educational technology: developing AI systems that combine the pedagogical rigor and personalization capabilities of Intelligent Tutoring Systems (ITS) with
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partners from ICube/Université de Strasbourg, RDP/ENS Lyon, and Inria Grenoble. The overall objective of the 4DPlants project is to develop new methods for the semantic and instance segmentation of time
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safety-critical tasks, such as healthcare or automated vehicles, where trustworthy models are required. The existing literature for the OoD detection problem focuses on the development of confidence scores
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existing literature to address this gap. The student tasks will be to prepare the registration documents (e.g., PROSPERO), refine and test the search algorithm in relevant databases, import search results
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Participate in the integration of Generative Artificial Intelligence algorithms and the enhancement of the recommender system. Is Your profile described below? Are you our future colleague? Apply now! Education
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6 Research Professors (DR2). Competitive entry is open for research themes pertaining to Physics M/F
6 Dec 2025 Job Information Organisation/Company CNRS Department CNRS Research Field Computer science Mathematics » Algorithms Researcher Profile Established Researcher (R3) Country France
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6 Dec 2025 Job Information Organisation/Company CNRS Department CNRS Research Field Computer science Mathematics » Algorithms Researcher Profile First Stage Researcher (R1) Country France
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(multilayer) low-rank tensor decompositions [5,6,7], which supply a rigorous mathematical framework to develop new algorithms and model architectures and to understand their behavior. These techniques will be
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collaboration with other CEA teams, notably ; * Parallel and cluster computing environment and efficient LP/MILP algorithms for our large-scale models ; * Data structuring and storage solutions for model input
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | about 2 months ago
follows a phased algorithm: 1) generate an initial training set by uniformly sampling input points 2) (re)train the model on the trainng set 3) use feedback from the model’s performance to generate/augment