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://www.inesctec.pt/pagamento-propinas-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: - Development and testing of algorithms and methodologies based
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: Proven experience in Knowledge Extraction from Data (i.e., data analysis, data preprocessing, Machine Learning algorithms); Knowledge in software development using Python; Knowledge in specifying and
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internationally. Main Responsibilities · Contribute to the installation, calibration, and validation of radio monitoring equipment. · Develop and test real-time data pipelines and deep learning algorithms for Solar
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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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this data. Clustering travel needs. To define different mobility needs and motivations based on the travel data, we apply different clustering algorithms (e.g., traditional k-means, density-based clustering
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solutions; • Implementing and comparing different Intelligent Optimisation algorithms. • Implementing, providing and monitoring intelligent solutions (e.g., via API). • Producing documentation
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of the state of the art in Evolutionary Algorithms and Large Language Models. Survey of the state of the art in Evolutionary Algorithms applied to Large Language Models. Implementation of an evolutionary
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Requirements: Selection will prioritize candidates with interest and/or experience in the following areas: a) Basic knowledge of machine learning techniques, with interest in exploring algorithms such as
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) Proficiency in scientific programming tools and development of machine learning algorithms applied to biomedical data (preferably Python and MATLAB); e) Skills in managing computer equipment for advanced
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available under the following conditions: OBJECTIVES | FUNCTIONS Development and evaluation of machine unlearning algorithms for speech foundation models, including: - Selection and preparation of models and