112 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" positions at Universidade de Coimbra
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Collaborate in the use of remote sensing and machine learning methods to detect A. longifolia and to monitor the spread and effects of the biological control agent (occasional collaboration). Activity 4
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Neuroscience on object representation and object properties using machine learning and multivariate techniques to analyze the data; support in writing scientific outputs. The grantee will also support the entire
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an emphasis on the development of methodologies and techniques for Evolutionary Computation and Machine Learning. Work plan: Review of the state of the art in Machine Learning and Deep Reinforcement Learning
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Grant Regulations. Where to apply Website https://apply.uc.pt/IT137-25-569 Requirements Research FieldEngineering » Computer engineeringEducation LevelMaster Degree or equivalent Research FieldEngineering
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Programs, if applicable. It is expressly mandatory to use the FCT logo available at http://www.fct.pt/logotipos/ and, when applicable, the logos of the European Union and the Operational Programme
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Grant Regulations. Where to apply Website https://apply.uc.pt/ Requirements Research FieldEngineering » Computer engineeringEducation LevelMaster Degree or equivalent Additional Information Work Location
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, and published on https://apply.uc.pt/ . VII.VIII - Jury Composition: VII.IX - Selection reserve list: the reserve list consists of candidates approved in all selection methods who do not secure one
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on the score of their academic qualifications. VII.VI - All provisional and final jury decisions must be justified, recorded in minutes, and published on https://apply.uc.pt/ . VII.VII - Jury Composition
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to secretaria.mia@uc.pt . Reference of the expression of interest: MIA-AR-2025-03 Scientific area: Data Science and Machine Learning Work plan/objectives: The “Continuous Learning Lab”, led by Dr José Sousa
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for developing machine learning models for the automatic identification of species from images collected through electronic monitoring systems (Work Package 3 – Bycatch Monitoring). The candidate will be involved