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quantitative geoscientific data. • Experience with one programming languages e.g. Python, Matlab, R. Critères essentiels : - Être titulaire d'un doctorat en géomorphologie ou dans une discipline étroitement liée
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at 900 K) * Developing automated workflows for data acquisition and analysis * Programming in Python and Matlab * Analyzing results and discussing them with the other project partners * Writing scientific
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neurofeedback would be an asset. • Proficiency in data analysis tools (Python, MATLAB, neuroimaging software). • Familiarity with human experimental protocols and clinical best practices. • Knowledge of ethical
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information * Applied mathematics, computational physics, or computer science - Strong programming skills (e.g., Python, PyTorch, TensorFlow) - Experience in one or more of the following areas: * Generative AI
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to continuously learn new skills, methods and concepts, and 2) to enjoy finding new solutions in the face of new and unforeseen difficulties. The ideal candidate has very good 1) python programming skills, 2
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/HRTEM, SAXS/WAXS, surface NMR) – mastery of at least two expected. • Data analysis proficiency (Python/Matlab/R) and statistics (regressions, multivariate analyses). Additional assets • Professional
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Technical skills : - Ability to handle large datasets (hundred thousands to millions of rows) using a programming language (e.g. R, Python, MATLAB) - Good knowledge of descriptive multivariate data analyses
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. • PIC simulation codes. • Data analysis using C++/ROOT and/or Python. • Detector simulation using GEANT4. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR7638-ARNSPE-001
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in Python, PyTorch/TensorFlow, and medical visualization tools (e.g., 3D Slicer, ITK-SNAP, MONAI...). - Mastery of deep learning platforms Tensorflow/Pytorch/scikitlearn - English: high level Website
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of Research ExperienceNone Additional Information Eligibility criteria Knowledge in steganography, steganalysis, and generated image detection. Knowledge in image generation. Proficiency in Python. Additional