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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | 8 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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to develop Computer Vision algorithm and Interfaces for collaborations with Historians teams. Specifically, the research will focus on structured data with clear repeated patterns, such as characters in a text
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26 Nov 2025 Job Information Organisation/Company CNRS Department Institut de Recherche en Informatique de Toulouse Research Field Computer science Mathematics » Algorithms Researcher Profile First
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Inria, the French national research institute for the digital sciences | Bron, Rhone Alpes | France | 27 days ago
unified understanding of behavioral adaptation. The project aims to: Develop and analyze computational models that capture flexible, multi-timescale learning and adaptation in recurrent neural circuits
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of algorithms for each use case and foundation model. The latter tasks will involve close interaction with the partners responsible for the development of the language models and the partners responsible
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Description Evaluation of the robustness of ML algorithms executed on a non-hardened accelerator through circuit-level fault-injection The project addresses the challenges of deploying AI applications
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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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18 Nov 2025 Job Information Organisation/Company CNRS Department VERIMAG Research Field Computer science Mathematics » Algorithms Researcher Profile First Stage Researcher (R1) Country France
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) To develop Deep Learning algorithms to significantly speed up probabilistic inference algorithms of current spatial birth-death models 2) To incorporate fossil stratigraphic and spatial information into a new