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applications, writing and speaking proficiency in English. Previous relevant work and scientific research experience in computational applications with focus on Artificial Intelligence methods. Preferred profile
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. Preference factors: - Outstanding academic performance in Software Engineering, Programming, Software Development, and Artificial Intelligence courses; - Advanced knowledge of programming and software
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artificial intelligence-based algorithms to optimise operation and predict anomalies in water distribution networks. The algorithms developed should identify patterns and anomalies that indicate the presence
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goal is to study the possibilities to apply artificial intelligence. Ref. 4 Identification and definition of procedures to digitizing and/or automating the tasks of the audiovisual team. This project
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systems, with a clear understanding of intersectoral scientific fields ranging from optical fibre communication design and implementation, components fabrication, artificial intelligence, and techno
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Institute of Systems and Robotics-Faculty of Sciences and Technology of the University of Coimbra | Portugal | about 2 months ago
an Information System able to increase the security of Professional Training Certificates in the Portuguese Public Administration, resorting to Artificial Intelligence tools. The candidates must have skills in
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staff position within a Research Infrastructure? No Offer Description Call is open for the award of 1 (one) Research Grant under the project SMAIS: Artificial Intelligence for Municipal Water and
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the following terms: . SCIENTIFIC AREA: Artificial Intelligence. . RECIPIENTS: Holders of an undergraduate degree in Computer Engineering or related areas who are enrolled in a master course in Computer
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- Work Plan / Goals to be achieved: The post holder will join the DETECT project, which aims to develop an artificial intelligence-based tool to identify individuals at clinical high risk for psychosis
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.; - Develop skills in artificial intelligence and machine learning techniques for analyzing operational data and detecting anomalies, using foundational model approaches (e.g., GridFM project, LF Energy