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systems where the candidate played an active role together with familiarity with deep learning methods. EVALUATION CRITERIA The selection will be based on the following criteria: CV: 50% Experience in
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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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learning methods to digital pathology Development of deep learning algorithms for the computational analysis of whole-slide images. The objective is to identify relevant biological features and to perform
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months to two years, or per longer period. 1. OBJECTIVES | FUNCTIONS Development of techniques aimed at enhancing the efficiency of complex ML pipelines, with emphasis on methods aimed at predicting
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. This component of the evaluation is expressed in a scale of 0 to 100. The Jury may interview the first three candidates with higher classification in person or by video conference. If an interview is conducted
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accelerators, and of applications exhibiting these patterns in performance-critical hotspots; Development of methods to streamline programming AI-enhanced systems, taking into account partitioning / mapping and
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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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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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, 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 to each
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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 evaluation score