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perception for robotics; machine learning. o An interest for approaches based on foundation models. o Proficiency in open-source libraries: Pytorch or equivalent, OpenCV, Open3D, PCL, etc. o Programming
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relevant to the project's theme and activities. Solid experience in molecular simulation and/or machine learning is required, along with a good knowledge of associated theoretical tools (experience in
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, clustering analyses, propagating location and other uncertainties...) of mid-ocean ridge catalogs, using standard, Bayesian and machine learning techniques. ⁃ Implement methodologies that improve estimates
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analysis and visualization, signal processing, and ideally machine learning. • Working knowledge of Distributed Acoustic Sensing (DAS) and its applications in seismology (appreciated). • Aptitude
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instruments and high throughput genomics that informs advanced numerical analysis methods (modeling, statistics, machine learning). Plankton encompasses all organisms roaming with marine currents. Those
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experimental parameters (time, temperature). To optimize these parameters, active learning techniques based on Bayesian optimization will be applied. In situ or ex situ characterizations (FTIR, ¹¹B/¹H NMR, HP