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recognition, if applicable). - Enrollment in a PhD program. Specific Requirements - Master's degree in Environment Engineering, or related areas (If the candidate holds a higher education degree obtained abroad
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than 14/20 (1 point); B. Knowledge of Interactive Systems Design, Cyber-Physical Systems, Predictive Maintenance Systems, Automation, Machine Learning and Artificial Intelligence, Sensor Networks
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for 1 research grant (Master’s Degree), enrolled in PhD or courses that do not confer academic degree, within the framework of project MagHeat with the reference - CETP/0006/2024 , financed by Foundation
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machine-learning methods for sample segmentation and classification. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: The fellow will join the INESC TEC team within the LIBScan project, carrying
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Group PhD students and students in the final year of a Master's degree with a strong focus on research topics combining digital technology and sustainability Academic Requirements current enrolment in a
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regarded member of its Council, the scholarship supported postgraduate study – up to three years for a PhD or one year for a Master’s degree – with funding of up to $10,000 per year. Dr. Richards
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for this grant: Requirement 1: - Be a student enrolled in a doctoral program in the area of Materials science, Machine Learning computational science, Coating and surface engineering a requirement to be duly
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application of statistical Machine Learning tools. WORK PLAN Collaborate in the following tasks of the project: a) Contribute to the design and development of the Life Cycle Assessment (LCA) system; b) Support
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Escola Superior de Design, Gestão e Tecnologias da Produção de Aveiro - Norte da Universidade de Aveiro | Portugal | 3 months ago
Regulations of the University of Aveiro. 5. Work Plan: This project aims to develop solutions based on Artificial Intelligence for optimizing additive manufacturing processes. Machine learning techniques will
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 3 months ago
, reference no. 2023.18249.ICDT, financed by national funds through FCT/MCTES (PIDDAC Workplan: Development of methodologies and machine learning algorithms for the detection of anomalous behaviors in flow rate