Turn your résumé and research goals into explainable professor matches, a balanced shortlist, personalized outreach, and meeting-ready context.
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Professors · Recommended
Every score is traceable to your profile and the professor's current work—not just shared keywords.
Stanford University · Trustworthy NLP & Human-Centered AI
91
fit score
Calibrated Explanations for Language Models
ACL 2026 · Strong overlap
Auditing Fairness in Human-AI Decisions
FAccT 2025 · Methods overlap
When Confidence Should Defer
EMNLP 2025 · Trajectory signal
Sample lab facts
Top programs in our database · faculty coverage
PhdFit turns the work you already do—reading résumés, papers, lab pages, and recruiting signals—into one explainable workflow.
Traditional search
Agent-assisted discovery
Dr. Sarah Chen
Strong fitStanford University · Trustworthy NLP
Recent papers align with trustworthy NLP
Calibration and fairness evaluation
1 PhD position published this cycle
Evidence grounded in recent papers, a current lab page, and this cycle's recruiting note.
Each agent owns a real step in the Workspace, while the full team keeps your evidence, shortlist, outreach, and meeting context connected.
Current mission
Sarah's paper evidence now powers both outreach and meeting prep.
Agent control room
Evidence pipeline · all context synchronized
Profile signals extracted
24 professors researched
Five-axis evidence ready
Reach / target / safer balanced
Paper-grounded draft prepared
Meeting brief compiled
PhdFit ranks faculty across five weighted Academic Fit dimensions, then shows opportunity and confidence separately so practical context never silently changes the score.
The Candidate Analyst reads your résumé and extracts research interests, methods, publications, experience, and skills.
The Professor Researcher indexes faculty profiles, recent papers, method signals, recruiting evidence, and advisor-environment evidence.
Five evidence dimensions produce the Academic Fit ranking with explicit, unequal weights.
Opportunity and confidence stay beside the score, while the Match Explainer shows why each professor ranked where they did.
Does your academic and research path support this advisor's work?
Available evidence from your education, research experience, publications, projects, and skills. Less candidate evidence can lower this dimension and is also reflected in confidence.
Use these signals to decide what to verify next. Neither one is a weighted Academic Fit dimension.
What do current practical signals say about applying this cycle? Recruiting statements, intake timing, funding, and location preferences. Missing inputs can use conservative fallback values, so verify the underlying evidence before applying.
This context does not change the Academic Fit score.
How complete is the evidence behind this match? Candidate and professor evidence coverage. Lower confidence means verify the available evidence, not that the fit is poor.
This context does not change the Academic Fit score.
Balances academic readiness, research alignment, methods, recent papers, and advisor environment.
Prioritize professors whose research topics overlap most with yours. Good if you already know the exact area you want to work in.
Prioritize professors who use the same research methods (e.g. causal inference, Bayesian, RL). Good if your technical toolkit is more specific than your topic area.
Emphasize how your education, research experience, projects, and publications connect to the professor's work.
Compare professors, draft outreach, rehearse the interview, and walk into the meeting with the same evidence—not four disconnected tools.
Advisor-fit pipeline
Profile
Résumé + preferences
Professors
Ranked with paper evidence
Shortlist
Reach / target / safer
Outreach
READY TO REVIEWPersonalized draft ready
Prep
Interview + meeting context
Outreach workspace
Target · Stanford University
Personalized email draft
Calibrated explanations and trustworthy NLP
Dear Professor Chen,
Your recent work on calibrated explanations connects directly to my fairness-audit research and the evaluation gap I found…
Interview prep
6 tailored questions + mock response scoring
Meeting prep
Papers, talking points, risks, and next steps
Move from basic discovery to unlimited matching, professor research, outreach drafts, and meeting prep. Pick the runway that matches your application timeline.
One application season. One payment. No renewal to manage.
Recommended for active PhD applicants.
Short sprint access when you need a focused push.
Free is the starting access level, not a fourth purchasable plan.
| Feature | Free | Weekly | Monthly | Season Pass |
|---|---|---|---|---|
| AI resume and profile analysis | Basic | Unlimited | Unlimited | Unlimited |
| Professor match ranking | One-time personalized Top 3 | Unlimited | Unlimited | Unlimited |
| Academic Fit explanations | Top 3 summary | Full | Full | Full |
| Research Scout signals | Not included | Included | Included | Included |
| Outreach Writer | Not included | Drafts + history | Drafts + history | Drafts + history |
| Meeting prep sheets | Not included | Unlimited | Unlimited | Unlimited |
| Shortlist export | Not included | CSV + notes | CSV + notes | CSV + notes |
| Application tracking | Manual | Manual + AI actions | Manual + AI actions | Manual + AI actions |
Fictional composite profiles shown for layout preview. Replace them with verified, authorized testimonials before launch.