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scholarship holder will contribute to several research activities, including: - Design and implementation of event-based data compression algorithms, including lossy and learning-based approaches. - Development
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quality of water stored therein; b) calibrate and validate models or algorithms based on spectral signatures, associated with in situ validation campaigns; c) extract indicators of spectral signatures
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capacity to process complex simulation data, fine-tuning its interpretation algorithms, and ensuring that gap-filling recommendations are both biologically plausible and supported by external resources
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will be processed by advanced Artificial Intelligence algorithms. The numerous tasks for this grant will contribute to the project's activities. Namely: Installation of sensors on telecommunications
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will be processed by advanced Artificial Intelligence algorithms. The numerous tasks for this grant will contribute to the project's activities. Namely: Installation of sensors on telecommunications
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and signature matching based on existing code. - Performance evaluation of the application on a Raspberry Pi (RPi). - Development of improvements to machine learning algorithms for anomaly detection and
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of historical and real-time data will be processed to improve crop profiles and tailor growth plans. The project includes using advanced algorithms to link various irrigation, nutrient, and telemetry datasets
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growth plans. Supported by correlation algorithms applied to telemetry, irrigation, and nutrient data, it ensures data integrity for both regulatory purposes and the execution of automated decision-making
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study its impact on the degree of collaboration in hybrid teams. The successful candidate will: Develop algorithms to model team performance based on interpersonal (e.g., monitoring, communication) and
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of the following tasks: Development of subsystems for high-speed optical communication systems, including pointing, acquisition and tracking, optical fiber coupling and digital signal processing (DSP) algorithms