Format CVs for academic positions with publications, grants, and teaching
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Rewrite resume bullets into natural engineering prose while preserving evidence, ownership, and technical meaning. Uses qualitative checks rather than AI-detection scores.
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Make evidence-backed bullets concise, natural, and interview-defensible.
Do not optimize for AI-detector scores. AI-detection tools are not a reliable measure of whether a resume is good or authentic.
Prefer:
Avoid inflated wording such as:
A technical bullet should name the actual system, technology, algorithm, API, database, or artifact when relevant.
Do not add:
Keep bullets compact. Split a bullet when multiple unrelated claims make it hard to read.
Include architecture/technical decisions only when they help explain the work; do not force a "why X over Y" comparison into every bullet.
The candidate should be able to explain every noun, number, and technical decision.
For each rewritten bullet:
Then run this checklist:
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Format CVs for academic positions with publications, grants, and teaching
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Decides whether to apply to a specific role, with what level of effort, and with which resume variant. Prevents wasted applications on roles with fundamentally poor fit and prioritizes high-probability opportunities.
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Decide how to prioritize job applications using hard eligibility, evidence coverage, role alignment, and real application outcomes rather than arbitrary fit scores.
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Define a clear primary role identity and supporting evidence so a resume reads coherently for the target job.
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Determines the candidate's primary role identity before any JD analysis. Classifies into role families, selects core tech identity, ranks projects by role relevance, and outputs a Candidate Position Brief consumed by all downstream stages. This is Phase -1 of the pipeline.
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Translate skills from one industry to another, identify transferable skills
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