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trigger reconstruction architectures for future particle collider experiments, based on deep learning models distributed across multiple hardware processing stages. The mission of this position, based
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, statistics, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology
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throughput genomics that informs advanced numerical analysis methods (modeling, statistics, machine learning). Plankton encompasses all organisms roaming with marine currents. Those organisms are responsible
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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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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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machine learning tools. The postdoctoral fellow will contribute to various aspects of the project, such as: * developing new theoretical and numerical approaches for determining the thermodynamic and
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in the Earth's outer core, with implications for deep Earth processes [1]. A variety of inverse methods (data assimilation, machine learning, etc.) has been employed to recover the fluid motions 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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a team More specifically: - For mission 1: knowledge of signal and image processing, machine learning (PyTorch or TensorFlow + NumPy/SciPy), statistical processing & data and results visualisation
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Machine/Deep learning and classification Knowledge of the Linux operating system for using a computing cluster Interest in transdisciplinarity and teamwork Autonomy and scientific rigor Website