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Field
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Grant(s) (RG) in the scope of R&D projects FireLSF - Development of predictive models for the fire resistance of light steel frame walls - an integrated experimental, numerical and machine learning
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PROGRAMME AND TRAINING: - extend the knowledge of the state of the art in machine learning for lung cancer imaging data; - identify and select the appropriate methods for the study in question; - develop
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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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and Technology of the Arts, Digital Arts, Conservation and Restoration, Data Science, Creative Computing, or related areas. • Knowledge of IoT, creative programming, machine learning, and/or data
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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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Website https://www.inesctec.pt/en/opportunity/AE2025-0532 Requirements Specific Requirements Academic qualifications: Training in Electrical and Computer Engineering. Minimum profile: • Be enrolled in a
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the following profile: Enrolled in a master’s degree in computer engineering or related fields; Knowledge of Extended Reality application development and/or knowledge of machine learning, information
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by the European Research Council - ERC COG 101088763. The work for this position is in the area of Machine Learning, Decision Theory, Reinforcement Learning. Scientific Area: Information and Data
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21 Nov 2025 Job Information Organisation/Company INESC TEC Research Field Computer science » Computer systems Researcher Profile First Stage Researcher (R1) Country Portugal Application Deadline 5
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 2 months ago
with setting up a streamflow forecasting system in Portugal and the advancement of scientific knowledge in machine learning probabilistic hydrological forecasting and decision-making optimized to act on