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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 7 days ago
. Practical knowledge of Python, clean coding practices, and reproducible ML workflows. Familiarity with protein structure, biophysics, or molecular modeling (MD, docking, etc.) is highly desirable. Ability
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related field) with a specialization in image processing and machine learning. They should demonstrate strong algorithmic programming skills (in Python, and possibly C++) and be comfortable working with
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source level with Python and MNE-Python - Pipeline development in Python and MNE-Python - Articles preparation, presentations and workshopsmantenimento di codice open source. The position is hosted
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. - Proficiency in Python and C/C++ for developing AI systems. - Experience in software security, vulnerability detection, and malware identification. - Ability to prepare and analyze datasets for model evaluation
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optionnels : Connaissance d’une langue autre que le français et l’anglais. Compétences techniques : Programmation en Python ; Git ; Javascript ; Html/Css Technologies full stack web Requêtes http et API “REST
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biomedical research. Your profile Master's degree in computer science or related discipline Experience with Python and recent deep learning frameworks (e.g. Pytorch, MONAI) Strong interest in image analysis
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to analyze and process experimental data (Python). - Rigor in experimentation, critical thinking, and synthesis skills for interpreting results and contextualizing them with scientific literature. - Ability
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Skills in multiphysics numerical simulation and related domains English language : B1 level Basic knowledge of programming languages (C,Fortran, Python, ...) Personal skills: Rigor, autonomy, analysis
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molecular dynamics methods as well as QM/MM methods and Python programming. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR6296-PASBES-007/Default.aspx Work Location(s) Number
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, simulation, and design of photonic devices (Python, Matlab, Lumerical) as well as with nanofabrication processes, in particular for silicon photonic devices. Some experience with the experimental