Format CVs for academic positions with publications, grants, and teaching
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Track resume variants, job applications, evidence changes, and observed outcomes so tailoring decisions can be evaluated from real data.
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Maintain:
Keep factual source data only:
Examples:
| Field | Example |
|---|---|
| Company | ExampleCo |
| Role | Backend Intern |
| Job URL | https://... |
| Variant | Backend_FullStack |
| Submitted file | Pinak_ExampleCo_Backend.pdf |
| Date | 2026-09-28 |
| Channel | Direct / Referral / Campus / Recruiter |
| Eligibility | Meets / Unclear / Mismatch |
| Required evidence | 6 direct / 1 indirect / 1 missing |
| Outcome | Pending / Assessment / Interview / Rejected / Offer / Withdrawn |
| Notes | ... |
Do not store a universal "match score" column.
Do not automatically convert "no response after N weeks" into rejection. Keep it Pending or Closed/Unknown depending on evidence.
After enough observations, compare:
Treat these as descriptive observations. Do not claim causal relationships from small samples.
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Format CVs for academic positions with publications, grants, and teaching
The English summary is being prepared. Showing the original description.
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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