264 machine-learning "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" "UCL" Fellowship positions in Portugal
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the following conditions: OBJECTIVES | FUNCTIONS The purpose is to continue the research on Machine Learning methods applied to optimization techniques, in particular for the veicule routing problem. The idea is
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Engineering. INESC-ID’s research impact is focused on four Thematic Lines Energy transition Life and health technology Security and privacy. Societal digital transformation. INESC-ID promotes cooperation
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Computer Science (or close field) with strong foundations in data management, machine learning, and software engineering. Coursework or projects in NLP/LLMs, information retrieval, knowledge graphs/ontologies, data
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compression, event-based or neuromorphic vision, signal processing, machine learning or deep learning for visual data. - Motivation for research and scientific dissemination. - Good communication skills in
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engineeringEducation LevelMaster Degree or equivalent LanguagesENGLISHLevelExcellent Research FieldEngineering » Computer engineering Additional Information Website for additional job details https
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Procedure Applications must be exclusively submitted on the admissions platform of the Instituto Superior Técnico at https://fenix.tecnico.ulisboa.pt/fenixedu-admissions and requires registration and
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related fields. Additional optional skills and qualifications: Experience in deep learning for medical imaging. Contracting requirements: Presentation of the academic qualifications and/or diplomas
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and qualifications: Experience in deep learning for remote sensing. Contracting requirements: Presentation of the academic qualifications and/or diplomas, if applicable. Enrolment in Master's degree in
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), of the Polytechnic of Leiria Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Undergraduate Positions Application Deadline 6 Mar 2026 - 23:59 (Europe/Lisbon
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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