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analysis, as many observed phenomena cannot be adequately modeled by stationary processes. The NOMOS project aims to develop a new generation of nonstationary models and algorithms for analyzing various
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nonstationary models and algorithms for analyzing various biological signals. The project will focus mainly on developing innovative models for biomedical signals with irregular cyclicity and exploring potential
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evaluation of algorithms for: perception in robotics; sensor based control and navigation ; interactive mobile manipulation; multi-sensor data modelling and fusion. This job offer takes place within
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2 Sep 2025 Job Information Organisation/Company CNRS Department Maison de la Simulation Research Field Computer science Mathematics » Algorithms Researcher Profile Recognised Researcher (R2) Country
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for: • Contributing to various tasks related to the modeling of lipids and membrane proteins involved in lipid droplet biogenesis. • Developing and implementing the POP-MD algorithm in the OpenMM software
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Laboratoire de Physique des Interfaces et des Couches Minces (LPICM), UMR CNRS/École Polytechnique, | Palaiseau, le de France | France | 29 days ago
(denoising, Mueller matrix calculation/decomposition) and AI-based diagnostic algorithms using machine/deep learning. The primary challenge will be to deliver practitioner-relevant cervical images in under 0.5
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the project goals. The candidate will be expected to work at 3 levels: 1 Infrastructure (data integration) 2 Integration of algorithms and the machine learning protocol (or even a contribution to development) 3
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the necessary data from numerical models and observations to build the dataset; Identify the algorithms best suited to learn the targeted behaviors; Train the learning models; Validate their ability to predict
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components: - operational modal analysis to extract the modes of the probed medium, - algorithmic and experimental developments on the MSE method - and algorithmic and experimental developments on the MFP
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learning, and generative AI Design and implement algorithms for quantum-inspired and quantum-enhanced generative models Investigate theoretical foundations of tensor networks, entanglement, and collapse