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Artificial Intelligence models for the accurate simulation of motor racing and for the generation of suggestions for effective racing strategies. The research fellow will explore different modelling approaches
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is necessary to: 1) explore platforms and frameworks that support the development and implementation of the models needed to detect and predict leaks; 2) process real and simulated data that will be
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plan consists of the following development phases: 1) Literature review Survey of the state of the art in pose detection, facial recognition, object detection, and activity detection models focused
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languages (Portuguese, Spanish, and English) for the recommendation mechanisms. Investigating and designing decision support mechanisms, including multi-objective computational models and risk assessment
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the application of security mechanisms in LLMs. (ii) Survey of requirements and planning of mechanism(s) that provide security for model input and output data in the context of code generation. (iii) Implementation
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, design, implement, and validate an optimization system for scheduling group classes within the Koachy platform. The specific goals include: Develop attendance prediction models for different types
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an agricultural context, in order to complement existing public datasets, which have enabled the validation of the solutions developed and, eventually, the improvement of existing models. This fellowship also aims
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: • Identifying use cases and modelling functional and non-functional requirements. • Defining the logical and physical architecture (frontend, backend, databases, AI services). • Full-stack implementation (modern
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: • Backend based on REST/GraphQL APIs that expose cork stopper catalogue functionalities, creation and management of final products, and consultation of machine learning model records; • Angular frontend
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of large language models (LLMs), risks and threats that affect their security in the context in question, protection/control mechanisms appropriate to the main risks, and appropriate evaluation methodologies