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Fellow (EB1)1, available in https://dre.pt/application/conteudo/127238533 and in accordance with the consolidated version with the changes resulting from the update, approved on December 10, 2025 by
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, particularly Python; (ii) use of parameter optimization algorithms, particularly PEST and PEST++; (iii) remote sensing applied to the water cycle; and (iv) application of machine learning techniques to spatio
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requirements: Experience using deep-learning algorithms. In-depth knowledge of Python and PyTorch. Previous experience collaborating on scientific projects. Publications on deep-learning topics. 4. Work Plan
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be duly proven at the time of hiring. 2; 3. Preferred requirements: Experience using Machine Learning algorithms. In-depth knowledge of Python and PyTorch. Previous experience collaborating
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of Coimbra III- Scientific supervision/coordination of the grant: Rui Paulo Pinto da Rocha IV - Work Plan / Goals to be achieved: 1. Development of algorithms for swarm robotics and human–swarm interaction 2
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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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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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an international call to hire 1 (one) Researcher, in form of an Unfixed-Term Contract and at full-time under the Research Project “SmartADC Design of a ultra high-speed time-interleaved ADC using genetic algorithms
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on methods to improve understanding of how machine learning algorithms work. Workplan: Literature review Design of an approach for the selected problem Empirical evaluation of the proposed approach Writing