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LLMs for comparative evaluation. Automate classification experiments across original and perturbed datasets. Classification Shift Analysis (Month 2) Measure classification changes between original and
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will be integrated into a research team with established expertise in software security, program analysis, and AI-driven code intelligence, with a track record of collaboration with leading technology
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insecure JavaScript code from open-source repositories. Identify and label security-related commits using diff-based analysis. Integrate synthetic data generation (e.g., AST-based vulnerability injection
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learning. (Month 5) Assess secure-by-default code generation using static analysis tools (e.g., Semgrep, CodeQL), measuring vulnerabilities per 100 lines of generated code. (Month 6) Ensure reproducibility
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Converter, namely Sucessive Aproximation and Sigma Delta topologies 2- Schematic design of the reconfigurable ADC 3- Schematic validation is corners and Monte Carlo analysis 4- Layout design of the ADC
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+ (frontend and backend) to improve usability and experience on multiple devices (laptop, tablets and phones). - to develop and integrate tools for quantitative transcriptomic analysis within YEASTRACT
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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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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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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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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