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and floods. The system will be dynamically updated, by integrating hazard prediction models, geospatial technologies (Remote Sensing, GIS, and GNSS), real-time field data, and advanced prediction models
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on temporal data (predictive maintenance, sensory processing for robot control, etc), in which efficient on-device processing is crucial. We are looking for a highly motivated PhD candidate with an interest in
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Internet of Things. Practical experience in Kubernetes, scaling of cloud applications, microcontrollers, predictive maintenance, Smart City fields. Research experience in cloud computing, edge computing, and
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on optimizing the continuous precipitation/crystallization of ammonia or ammonium from leachates using a laboratory-scale OFR. During the work, the influence of several variables controlling ammonia or ammonium
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for predictive policing and surveillance, as well as data mining and threat detection to name some of the most prominent uses. This position will enable you to develop top-notch research in the areas of AI and
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aseptic and sterilization techniques Knowledge of infection control measures Interpersonal skills necessary to work effectively with patients Ability to work efficiently Ability to make decisions based
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these principles, the programme aims to transform our understanding of how tissues form, adapt and become disordered, laying the foundations for predicting and ultimately controlling these processes. You will
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, experimental prediction, control systems, and solutions to partial differential equations. To establish and solicit long-term funding for a program focused on (a) foundational research in Artificial Intelligence
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contribute to the development of predictive human in vitro models for ageing research while providing robust alternatives to animal experimentation. Where to apply Website https://emploi.cnrs.fr/Offres
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for a research grant within the framework of the Funding UID/00511/2025 of the Research Unit LEPABE - Laboratory for Process Engineering, Environment, Biotechnology and Energy, and DOI https://doi.org