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measurements, extract key magnetic parameters (coercivity, saturation magnetization), and interpret the results. • Data processing: Strong command of tools such as Python for numerical data analysis
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biomechanical modeling, ideally applied to rigid multibody systems and to the musculoskeletal system. - Python programming (NumPy, SciPy, scikit-learn); interest in statistics, machine learning, model
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management - Expertise in programming languages: R Studio, MATLAB, Python - Proficiency in the development and implementation of neurophysiological recording tools (EEG, EcoG, etc.) - Proven experience of
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experience in spectral imaging techniques (e.g., software such as Python, R, HyperSpy) and/or synchrotron techniques (luminescence, UV-VIS-NIR). Experience in hyperspectral data processing (HMSPL, μXRF
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. Experience with image analysis software (ImageJ/Fiji) and scientific computing tools (Python, R or equivalent) will be appreciated. Good command of spoken and written scientific English. Personal
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computational biology, machine learning, bioinformatics or a related field. • Proficiency with Python programming. • Proven experience in deep learning (PyTorch or TensorFlow). • Previous experience with
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knowledge of shell scripting, FORTRAN, Python, or R, and experience working with NetCDF files Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8190-SOPBAS-005/Default.aspx Work
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• Machine Learning applied to physics — good to confirmed level • Materials thermodynamics — good level • Experimental data analysis (forces, fields, imaging) — good level • Scientific programming (Python
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, etc.). 2) Modeling and data analysis: - Proficiency in GIS tools (QGIS, ArcGIS) and hydraulic modeling software (HEC-RAS, TELEMAC, Mike). - Advanced programming (Python, R, MATLAB) for data processing
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: ---------------------------------------- The candidate must hold a Master's degree in mechanical engineering or materials science. Knowledge of coding (Python, C, Fortran) and tribology is desirable. Where