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EXPERIMENTAL, NUMERICAL AND MACHINE LEARNING”, funded by the “Programme, Innovation and Digital Transition (Compete 2030), European Regional Development Fund FEDER and national funds, Portugal 2030, Foundation
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for applications for one research grant within the framework of project ISA4RL - Integrating Instance Space Analysis with Auto-Reinforcement Learning for Adaptive Algorithm Selection and Configuration
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Engineering or Industrial Engineering and Management) - 10 points; Others Masters – 2 points) b) Experience in applying machine learning algorithms, data preparation, normalization, feature selection, and
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–5): selection of relevant climatic variables and application of statistical modelling and/or machine learning techniques to predict risk. 3) Preliminary validation of the predictive model using
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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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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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, Economics, Management, or related fields. [1] ; Be a student enrolled in a doctoral program in Computer Engineering or Computer Science - a requirement to be duly proven at the time of hiring. 2; 3. Preferred
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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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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