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Experience: Work developed related to the work and functions to be performed (punctuation: 30) Item 03: Specific Education: Programming microcontrollers, sensors and IoT communications (punctuation: 25) Item
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, integrated circuit designers, and quantum algorithm developers Analyze and interpret experimental data, contributing to scientific publications, patents, and presentations. Engage with the wider international
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/Qualifications Experience in: - Development of artificial intelligent algorithms. - Explanaible artificial intelligence - LLM experience - Virtual intelligence entities using reinforced learning
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of chemoreceptor (CR) genes and other sensor proteins in phytopathogenic bacteria. Biochemical charactherization of CRs and sensor proteins. Functional charactherizations of mutants in the context of plant
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for precision livestock farming within the Horizon Europe Re-Livestock project. The candidate will design and analyse mathematical algorithms to monitor and improve animal performance, welfare and environmental
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STUDENTS FUNDED BY THE UPV'S RESEARCH STRUCTURES – SUBPROGRAMME 2 (PAID-01-22) 119977 Development of mathematical and machine-learning algorithms to support an intelligent, integrated system for biosafety
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Research Centre for the Management of Agricultural and Environmental Risks (CEIGRAM) - Universidad Politécnica de Madrid (UPM) | Spain | about 2 months ago
information from aerial and ground-level sensors. • Knowledge of the processes of extracting data from sensors and analyzing the results. Specific Requirements • High level of English. • Graduate in
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title: UDC-Inditex Chair of Artificial Intelligence in Green Algorithms Research line / Scientific-technical services: Development of algorithms that are energy efficient Grant/funding period: START: 01
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title: UDC-Inditex Chair of Artificial Intelligence in Green Algorithms Research line / Scientific-technical services: Development of algorithms that are energy efficient Grant/funding period: START: 01
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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