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processing (computer vision & machine learning) Where to apply Website https://sede.uvigo.gal/public/catalog-detail/28364578 Requirements Research FieldEngineering » OtherEducation LevelMaster Degree
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) and satellite platforms, and surface energy balance models will be used to obtain evapotranspiration (ET); computer vision and machine learning techniques will also be used to identify and count fruits
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participative digital platform that encourages users to contribute through crowdsourcing, oral interviews, or sharing privately held materials. Implementation of AI and machine learning techniques, including
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Engineering). - Theoretical foundations of 6G RAN and autonomous systems o Proven knowledge of AI-native RAN systems. Indicative skills/experience: - Deep understanding of 5G/6G RAN architecture (O-RAN, NG-RAN
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scalable and efficient software and firmware analysis methodologies using tools like Frida or IDA Pro, enhanced with traffic monitoring techniques and Machine Learning to detect and analyze vulnerabilities
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classification of EEG and auditory signals. The group of the project is multidisciplinary, with experts in signal processing, machine learning, acoustics and language. The successful applicant will perform
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), técnicas y herramientas software de análisis de datos, machine/deep learning (Pandas, SHAP, TensorFlow, etc.) y específicas de análisis de imágenes, estadística, simulación, entornos cloud (tipo Kubernetes
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or equivalent Skills/Qualifications Valued/Preferred qualifications: Experience in software solution development Experience with machine learning models Experience in R&D&I projects (Research, Development and
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on the application of machine learning in satellite communications (20 points). Participation in European Space Agency projects (20 points). Other skills that are valuable, but not mandatory are: Knowledge of over
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remuneration, or a proportional part for periods of less than one year. 2.5. Tasks to be carried out: · Developing advanced tools for the spectral analysis of the X-IFU instrument. · Developing machine learning