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Description Conduct a part of the ANR MetaTime (setting-up experiments, acquisition and processing of data, writing scientific reports) • Perform a review of the existing litterature on the topics • Acquire
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towards scalability and error correction, we are adopting a measurement-based computing paradigm. This model assumes that it is possible to generate light states in which a large number of photons
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visualization. Experience with GWAS, Bayesian modelling, and/or machine learning applied to biological data. Strong programming skills (R, Python) and ability to manage large-scale -omics datasets. Good
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cellular biophysics -Backround in microfluidics -Background in image processing Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR5588-CHAMIS-012/Default.aspx Work Location(s) Number
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of teleworking and flexible organization of working hours Professional equipment available (videoconferencing, loan of computer equipment, etc.) Social, cultural and sports events and activities Access
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-criteria, defining their formalization as fuzzy subsets, and characterizing their uncertainty; Integrating Machine Learning algorithms to better account for low-level sensor data (precipitation, wind-driven
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in the area of scientific computing and Computational Fluid Dynamics. Prior Experience in turbulence modelling, machine learning or the Lattice Boltzmann method is an advantage. Operational skills
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results in leading conferences and journals Required Qualifications PhD in one of the following areas (or related fields): Machine learning / deep learning Quantum computing / quantum information Applied
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(electron microscopy, micro-characterization and functional morphohistology- imaging), BIO3 (biomaterials, biohydrogels and biomechanics), INOA facility (OsteoArticular INflammation), HiMolA (Molecular
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Western blotting Experience with imaging techniques is considered an advantage Strong communication and organizational skills Proficiency in written and spoken English is required We offer Multilingual and