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and optimisation algorithms, focusing on their practical application in the context of the RaceEngineerAI project. Tasks include: - Developing models capable of simulating the behaviour of racing
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | about 23 hours ago
for Science and Technology, I.P., both in their current wording. 2. Activity objective: Research and implementation of advanced Optimization and decision-support algorithms using Artificial Intelligence applied
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symbolic language. This foundation supports a novel alignment algorithm that quantifies inconsistencies, offers actionable compliance scores, and exposes bottlenecks across control-flow, data, and resource
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recommendation mechanisms based on semantic analysis and natural language processing, with the aim of facilitating collaboration and convergence of proposals. Developing and training NLP algorithms in multiple
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benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: Research and develop novel reliable deep learning computer vision algorithms for the detection and quantification of GIM lesions
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, considering discrete modulation; Contribute to the implementation of digital signal processing algorithms in a FPGA platform; Contribute to the implementation of information reconciliation algorithms in a GPU
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to or higher than 16 points. 4. Work Plan: 4.1. The purpose of this contract is to perform the following tasks: Literature review in artificial intelligence algorithms for Fairness in Online Learning algorithms
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Artificial Intelligence, with an emphasis on developing methodologies and techniques for Evolutionary Computation and Machine Learning. Work Plan: State-of-the-art survey of Evolutionary Algorithms and Large
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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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clinical data and machine learning algorithms. The main activities include: Data Processing: • Collection of historical patient data (demographics, clinical history, outcomes of interventions). Data cleaning