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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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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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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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of classes, using Machine Learning (ML) techniques such as Decision Trees, K-Nearest Neighbors (KNN), XGBoost, Support Vector Machines (SVM), or Neural Networks. Explore and implement clustering algorithms
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computer vision algorithms to detect clinical interventions performed by nurses and situations of agitation and risk of falling. Volume of data available for the project: Video capture in a hospital