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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 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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researcher will be hosted by the ECOPAR group at the Centre for Functional and Evolutionary Ecology (CEFE) in Montpellier, led by Cyrille Violle. ECOPAR specializes in plant functional ecology but also works
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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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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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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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· 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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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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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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team composed of researchers, engineers, bioinformaticians, biostatisticians and a project manager. The ideal candidate should hold a Ph.D. in Population genomics, Statistical Genomics, Evolutionary