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the Grenoble synchrotron. You will join the group responsible for developing diffraction analysis algorithms at the Grenoble synchrotron. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR5510
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. Experience conducting or supporting user studies involving patients or vulnerable populations. 4. Knowledge of real-time control algorithms for assistive or rehabilitation systems. 5. Experience
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. The work includes theoretical analysis, algorithm design, and validation on realistic case studies, as well as dissemination of results through publications, open-source code, and collaboration with internal
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Group , a leader in innovative multi-sensor atmospheric remote sensing from ground, airborne, and satellite platforms. Our group develops advanced algorithms and data analysis methods to address
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, the candidate will be leading the building of a database to i) guide neuroethological studies (behaviour and physiology) in honeybees at University of Konstanz, and ii) train algorithms aimed at modelling and
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algorithms, ideally in the healthcare or mental health domain Desirable criteria Track record of successful research grant applications, or attempts to obtain grant funding. Previous development of ML-based
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Université Grenoble Alpes, laboratoire TIMC, équipe GMCAO | Grenoble, Rhone Alpes | France | 3 months ago
Requirements Specific Requirements This project will require a candidate with experience in: Image registration algorithms (non-rigid is a plus) Advanced Python programming skills (especially with Torch / Keras
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network modeling to address real-world policy challenges through algorithm development and technical analysis. Key Responsibilities Conduct original research in generative AI Train and supervise graduate
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 2 months ago
dedicated Engineers responsible for the actual implementations. Hence, your primary focus will be on the creation of sound algorithms and methods, ensuring their theoretical integrity and applicability
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project is to develop a high-performance computing framework for mass spectrometry proteomics to enhance efficient processing and interpretation of large datasets using deep learning algorithms and GPU