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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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ESRF - European Synchrotron Radiation Facility | Grenoble, Rhone Alpes | France | about 2 months ago
years' relevant professional experience; Strong working knowledge of Python; Solid understanding of metadata concepts and common data formats; Experience in developing desktop and web-based graphical
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Computer Science, Engineering, Natural Sciences, or a related discipline. Minimum of two years’ relevant professional experience. Strong working knowledge of Python. Solid understanding of metadata concepts and
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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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for interdisciplinary research at the interface between algorithms and hardware, as well as the ability to work both independently and within a research team. Proficiency in Python programming (e.g., PyTorch
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problems, statistical learning and machine learning (machine learning, deep learning) - Knowledge of associated software development tools and environments: Python, PyTorch, Scikit-learn, Jax, Julia
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experience (perception, prediction, planning/control integration) Proficiency in Python and modern ML tooling is expected, along with familiarity with a quantum SDK (e.g. Qiskit). 5 How To Apply Interested
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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
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Strong background in differential equations and numerical methods Solid programming skills e.g. Python, C++, Julia or similar Interest in interdisciplinary research bridging mathematics and environmental
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novel machine-learning methodologies Excellent programming skills in Python and familiarity with modern ML tooling and reproducible research practices Experience training and deploying machine-learning