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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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to contribute to the development of fundamental aspects of computer science (models, languages, methods, algorithms) and to develop synergy between the conceptual, technological and societal challenges associated
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· Carry out research activities under the Department's contracts in the RS2M field · Participate in and ensure the delivery of project deliverables · Design and develop models, algorithms, and
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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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the Grenoble synchrotron. You will join the group responsible for developing diffraction analysis algorithms at the Grenoble synchrotron. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR5510
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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
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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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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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, complex systems. Strong communication and writing skills; ability to work both independently and as part of a team. About the team The DATA team develops foundational mathematical and algorithmic approaches
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/S0022112006003429 [2] A. Cahuzac, et al. “Smoothing algorithms for mean-flow extraction in large-eddy simulation of complex turbulent flows”, Physics of Fluids 1 December 2010; 22 (12): 125104, doi:10.1063/1.3490063