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architectures for explainable dual-process computation Design and development of deep neural network architectures and algorithms for the implementation of dual process computation approaches that improve
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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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to their classification consisting of 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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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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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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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