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) Interpretable machine learning for network adaptation. In this thesis, the student will study how interpretable models and explainable learning algorithms could be used in real cellular networks for safe
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of federated algorithms that enable fair management of the advantages and duties of energy communities in which vulnerable people live. The automatic generation of data models using AI technology will enable
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implementation of security provisioning algorithm in DFL environment Where to apply Website https://sede.uvigo.gal/public/catalog-detail/28364578 Requirements Research FieldEngineering » Communication
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apple orchards. The research will leverage the use of mechanistic crop models, remote sensing, sensors and computer vision. Remote sensing imagery will be acquired from both unmanned aerial vehicles (UAVs
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functionalities, studying their dispersibility, interaction with the matrix, and associated phenomena. Planned applications include sensors, optoelectronics, and security markers, with the goal of transferring
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. Specifically, NIR sensors, hyperspectral imaging coupled with standard or macro lens, spatially resolved spectrometry, evolving plots, and FTIR will be used for the non-invasive characterisation of raw material
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). Methods include sensor-based technologies, external and internal exposome approaches, and co-creation and participatory research methods. This PhD position seeks to contribute to and build upon work being
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-driven proteomic. Who are we looking for? A motivated researcher with strong programming skills and knowledge of machine learning algorithms, bioinformatics, proteomics, and molecular biology, keen to