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» Algorithms Researcher Profile First Stage Researcher (R1) Country France Application Deadline 27 Nov 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1
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4 Oct 2025 Job Information Organisation/Company CNRS Department Laboratoire d'informatique de modélisation et d'optimisation des systèmes Research Field Computer science Mathematics » Algorithms
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28 Aug 2025 Job Information Organisation/Company CNRS Department Laboratoire d'informatique de modélisation et d'optimisation des systèmes Research Field Computer science Mathematics » Algorithms
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | about 1 month ago
to compare base approaches (no active learning) with different state-of-the-art active learning algorithms including our solutions; Participate to the redaction of a publication exposing the methodologies and
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the values are drawn independently from known distributions. These results have been widely applied in areas such as auction theory, resource allocation, and stochastic optimization. The interdependent value
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the nature of the ambient noise in the reactor (spectrum, spatial and temporal distributions, levels), the approach consists in developing passive methods by determining their conditions of use but not
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Python-based frameworks or a web application) enabling image upload, AI-based analysis, and visualization of bacterial phenotypes. Perform image analysis using existing deep learning algorithms developed
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5 Sep 2025 Job Information Organisation/Company CNRS Department Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis Research Field Computer science Mathematics » Algorithms
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tomography (SPECT) is an imaging technique that tracks the 3D distribution of a radioactive tracer administered to a patient to monitor certain biological functions. The long acquisition times (10-40 minutes
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(MERCE). The main objective is to develop safe planning and reinforcement learning algorithms with various degrees of confidence for variants of Markov decision processes. More precisely, we will develop