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of massive galaxies from the primordial Universe to z~2. This project combines a unique JWST dataset with state-of-the art hydrodynamical simulations and machine learning techniques to understand the origins
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that interaction represents the foundation of active learning and fosters acquisition and retention of knowledge, as opposed to passive reception in traditional teaching. Some benefits of MR are now well established
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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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. • Strong knowledge of signal processing methods and machine learning. • Familiarity with regulatory and ethical constraints in research involving sensitive data. • Ability to work closely with
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for such applications. To respond to these challenges, this project aims to investigate automated decision making based on machine learning. The candidate (H/F) will propose and validate centralized as
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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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. - Knowledge in programming, data treatment, electron diffraction simulations, mathematical skills, knowledge about machine learning and artificial intelligence is a plus. Website for additional job details
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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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innovative methods for processing and analyzing 7Tesla MRI images of different modalities and formats (NIFTI, DICOM, etc.) using machine learning and artificial intelligence techniques. These methods will be
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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