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insurance, supported by INESC TEC. 2. OBJECTIVES: Over the years, large-scale infrastructures have been developed to support high computing performance without worrying about energy consumption. With
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neural networks, enabling us to estimate the reliability of a single decision of this algorithm. Regarding generalisation, recent self-supervised learning paradigms have strong synergies with the multi
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applicability to new domains, including HPC systems, in order to identify different usage alternatives (e.g., user-space execution). It also intends to design and develop new features that extend and improve
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be qualified for the individual interview. The Final Grade (CF) is obtained by the weighted average of AC (70) and EI (30). DISABILITY INCENTIVE Candidates who present a degree of disability equal
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be qualified for the individual interview. The Final Grade (CF) is obtained by the weighted average of AC (80) and EI (20). DISABILITY INCENTIVE Candidates who present a degree of disability equal
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on absolute merit. The top five candidates approved on absolute merit will be qualified for the individual interview. The Final Grade (CF) is obtained by the weighted average of AC (80) and EI (20). DISABILITY
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qualified for the individual interview. The Final Grade (CF) is obtained by the weighted average of AC (80) and EI (20). DISABILITY INCENTIVE Candidates who present a degree of disability equal to or greater
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approved on absolute merit will be qualified for the individual interview. The Final Grade (CF) is obtained by the weighted average of AC (80) and EI (20). DISABILITY INCENTIVE Candidates who present a
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merit. The top five candidates approved on absolute merit will be qualified for the individual interview. The Final Grade (CF) is obtained by the weighted average of AC (80) and EI (20). DISABILITY
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completed Degree;; Holder of a Degree in Biomedical Engineering or related field, with an average grade of 15 or higher; The awarding of the fellowship is dependent on the applicants' enrolment in study