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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 3 months ago
accurately study the selected algorithms, participate in the development and maintenance of the PEPit (https://pepit.readthedocs.io/ ) and AutoLyap (https://github.com/AutoLyap/AutoLyap/ ) software packages
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or MATLAB, integrating data preprocessing, feature extraction, and classification for hyperspectral and fluorescence lifetime datasets. Optimize algorithms for batch processing and scalability, enabling high
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methods, including UAV-based landscape mapping, terrestrial and freshwater eDNA, passive acoustic and camera monitoring, and novel sensor and logger networks for real-time analysis of greenhouse gas
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Setup and operation of a PET-SCAN prototype using MCPs as sensors - Design a vacuum
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data, including data from electronic health records, medical images, and sensor data from wearable devices Responsible for writing up results, training and supervising students and junior staff
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-modal data from electronic health records, medical images, and sensor data from wearable devices Responsible for writing up results, training and supervising students and junior staff, preparing
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, Physics , quant-ph , Quantum Science + Quantum Information Science + Quantum Optics + Theoretical Physics , Quantum Sensors , Theoretical Particle Physics , Theoretical Physics, HEP-Phenomenology (hep-ph
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artifacts, and developing an independent research agenda in AI for science. Core responsibilities include: Leading research on foundation models, including problem formulation, algorithmic development, and
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platform using AI. Involve in the development, implementation of the platform, as well as integration with multi-modal data from electronic health records, medical images, and sensor data from wearable
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between various imaging modalities and multi-omics during aging and development. • Implementing computationally intensive algorithms on high-performance computational clusters