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and with the 2AT team at Institut Pprime to develop a shape-optimisation tool based on resolvent analysis, applied to landing-gear aeroacoustics The researcher will develop a numerical methodology based
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“on-the-fly” approaches to efficiently process large volumes of data. The candidate will then perform benchmarking of these techniques to evaluate their performance and relevance for the analysis of airframe
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and with the 2AT team at Institut Pprime to develop an innovative jet-noise prediction tool. The researcher will develop a novel jet-noise prediction tool based on a resolvent analysis of the Navier
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cryogenic conditions. - Implementation and monitoring of hyperfine spectroscopy experiments (ESEEM/HYSCORE) in close collaboration with the engineers of the EPR platform. - Analysis and interpretation
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, including machine learning and language technologies, for the integration and analysis of clinical, advanced data harmonisation, and next generation research infrastructures. You will contribute to research
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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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characterization methods (basic skills) Analysis of biological fluids and culture media (basic skills) Ability to work independently on new topics. Strong analytical and synthesis skills. Ability to write reports
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on information and digital technologies (computer science, automation, robotics, artificial intelligence). Its aim is to develop methods for the representation, analysis, and control of systems subject to various
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learning applied to dynamic systems; Proficiency in key machine learning libraries (PyTorch, JAX, etc.); Mastery of Python and the software ecosystem for scientific data analysis and management (NumPy
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, testing translation modifiers and assisting with the analysis. Your profile Cell culture experience is essential, ideally with stem cells Practical experience in transcriptomics and, ideally, translatomics