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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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who have experience in software development (for example, developing full-stack applications), particularly as part of extra-curricular activities (such as internships or side projects). EVALUATION
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who have experience in software development (for example, developing full-stack applications), particularly as part of extra-curricular activities (such as internships or side projects). EVALUATION
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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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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 abstentions are not allowed
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of the partial classifications 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
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points, ranking the candidates according to their classification consisting on the sum of the partial classifications assigned in each evaluation criterion, and considering the weighting factor given
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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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the candidates 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
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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