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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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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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the educational project of a higher education institution, developed in association or cooperation with one or more R&D units. Note: In the case of masters who are enrolled in non-academic degree courses
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the educational project of a higher education institution, developed in association or cooperation with one or more R&D units. Note: In the case of masters who are enrolled in non-academic degree courses
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his/her scientific training. In particular, it is intended to develop and apply remote sensing methodologies and tools for the detection and modelling of habitats, as well as the extraction
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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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Science Objectives: The goal of this activity is to develop and validate methods for analyzing large data sets—Big Data Analytics—applied to the digital twin of hydroponic crops. Extensive amounts
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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