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refine, formalize, and extend an existing perturbation methodology to construct a principled, security-aware dataset of real-world vulnerable and secure JavaScript code. The work plan includes: 1
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JavaScript code using contrastive learning with a tailored security-aware loss function. The student will fine-tune selected models using secure-insecure code pairs derived from Tasks 1 and 2 and evaluate
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of Large Language Models (LLMs) in distinguishing secure from insecure JavaScript code. The student will design and implement a systematic evaluation pipeline to assess model behavior under perturbation
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Reference Number AE2026-0057 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2026-0057.pdf CALL FOR
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Reference Number AE2026-0036 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2026-0036.pdf CALL FOR
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utilizing modern programming tools and languages tailored to the project's requirements (e.g., Python, JavaScript, Django, among others). The activities may also include testing and validation on both