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Bayesian statistics, stochastic modeling, and optimization under uncertainty • Proficiency in Python programming • Strong interest in applied and transferable research • Knowledge of industrial simulation
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applications from candidates that hold a PhD in Physics or a related area. The successful candidate is expected to have an excellent background in soft matter physics, experience in programming (e.g., python, C
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candidate has experience in coding numerical solutions to partial differential equations via the finite element method / finite differences schemes and programming skills in Mathematica, Matlab, Python
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Eligibility criteria Strong skills in analyzing single-cell transcriptomic data using Python. Strong skills in analyzing epithelial tissue dynamics. Website for additional job details https://emploi.cnrs.fr
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molecular spectroscopy and nonlinear optics, and is a must for recruitement. Additional qualifications such as training and supervision of master students, and proficiency in Python and/or Matlab are valuable
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, hydro-acoustics, data science, geodynamics, geophysics, statistics, Bayesian inference ⁃ Experience with statistical analyses and machine learning techniques ⁃ Programming in C / python / Julia
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knowledge in at least of the following techniques : EEG, MRI, TMS • Good programming skills (python, R, matlab). • Knowledge of legal and deontological frameworks • English : B1 to B2 Know-how • Organise
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, computer science (medical image processing), medical physics. Computer skills: Python, C++ (ITK, RTK). Languages: English required, French optional. Website for additional job details https://emploi.cnrs.fr/Offres
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of experimental statistics, signal processing and excellent programming skills (eg Python, R, ...). Previous experience with model-based fMRI experiments, advanced multivariate data analysis and computational
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processing and modelling • Rock mechanics, and inversion • Computation: Linux, Matlab or Python Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR6524-AUDCHA-035/Default.aspx Work