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improvements in classification stability and security-aware code generation. The work plan includes: (Month 1) Implement contrastive learning fine-tuning using a tailored Multiple Negatives Ranking Loss (MNRL
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-induced variations. The work plan includes: Evaluation Pipeline Development (Month 1) Implement a scalable evaluation framework using local LLM infrastructure (e.g., Ollama, LMStudio). Integrate multiple
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existing vulnerability-mining tooling to ensure precise commit-level alignment and reproducibility. 2. Formalization and Extension of Security-Preserving Perturbations (Month 2-3) - Collect/define and
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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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software engineering practices – 15% Evidence of structured development, version control usage, and clean implementation. Motivation letter and alignment with project objectives – 20% Assessment
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data pipelines and ml/nlp components. code quality and reproducibility indicators (portfolio, github, reports, project deliverables). Project fit and motivation (20%): alignment of interests with kag
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data pipelines and ml/nlp components. code quality and reproducibility indicators (portfolio, github, reports, project deliverables). Project fit and motivation (20%): alignment of interests with kag