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aims to harness the power of Data Science, Process Mining, LLMs and Artificial Intelligence to optimize public administrations (PA) processes. The main objective is to face the challenging problem
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aims to harness the power of Data Science, Process Mining and Artificial Intelligence to optimize public administrations (PA) processes. The main objective is to face the challenging problem
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also implement Active Learning strategies to enhance the model's training process. Active Learning will enable the model to identify and query the most uncertain predictions for human labeling. By
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factor given to each parameter. In this process abstentions are not allowed. In the event of a tie among candidates with the same highest evaluation score, the Evaluation Panel reserves the right
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process includes the possibility of a professional selection interview for candidates with an assessment higher than 80% in the first phase. Candidates with an assessment lower than 70% will not be
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to each parameter. In this process abstentions are not allowed. In the event of a tie among candidates with the same highest evaluation score, the Evaluation Panel reserves the right to conduct interviews
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assigned in each evaluation criterion, and considering the weighting factor given to each parameter. In this process abstentions are not allowed. In the event of a tie among candidates with the same highest
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according to their classification consisting on the sum of the partial classifications assigned in each evaluation criterion, and considering the weighting factor given to each parameter. In this process
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this process abstentions are not allowed. In the event of a tie among candidates with the same highest evaluation score, the Evaluation Panel reserves the right to conduct interviews to have a more objective
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assigned in each evaluation criterion, and considering the weighting factor given to each parameter. In this process abstentions are not allowed. In the event of a tie among candidates with the same highest