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of this thesis is to study the dependability characteristics of the Cosmos blockchain and develop methods and techniques to automate the testing of the PLD blockchain platform. BINDING LEGISLATION Law 40/2004
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clinical texts according to the ICD taxonomy, (b) explainability methods to be used in connection with models for clinical coding. BINDING LEGISLATION Law 40/2004 of 18th of August (Scientific Research
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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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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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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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. 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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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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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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, 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