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data (nationwide LiDAR coverage at 50 cm resolution). The candidate will perform quantitative morphometric analyses of landscapes and river networks near suspected active faults using GIS tools, Python
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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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proficiency in Python. Knowledge in Statistical physics and Network science, and interest in complex networks and interdisciplinary research are a plus. The position is for 3 years, and will be located
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validation on simulated and real data Skills Abstraction capabilities, strong programming skills in C/C++ and Python, collaboration skills. English is needed. REFERENCES [0] C. Bekker, S. Rothmann, M. Kloppers
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programming languages like Python or Matlab environment. Knowledge for programming (python) would be a plus for the force curves interpretation since some codes have been developed for the 3D -AFM data analysis
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opportunities offered by cutting-edge CFD methods. Prior experience in CFD and/or CFD code development (e.g., Python, Fortran, C++) would be a considered as a strong asset. Selection process The recruitment
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about shape optimization and the new opportunities offered by numerical design in turbomachinery. Prior experience in CFD and/or CFD code development (Python, C++, Fortran) would be a significant
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experiment on a metric-scale concrete wall, using the multi-physics NDE probe integrated onto drone platforms. Expected Results A Python-based multi-physics Maching Learning framework for NDE characterization
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practice of English (level B1 desired) • In-depth knowledge of the Python language • Autonomy, interpersonal skills and ability to work in a team Website for additional job details https://emploi.cnrs.fr
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, computational biology, bioinformatics, data science, or related fields Strong interest in clinical and biomedical data, translational research, and health informatics Experience with programming skills in Python