We welcome PhD candidates/PhDs and industry experts with strong research or product experience and a passion for teaching high-school students. Priority fields include AI/ML, biology/health, behavioral economics, climate/policy, humanities, and interdisciplinary topics. Coaching experience and clear communication are essential.

• Mentors guide—not ghostwrite. You’ll:
• Help students frame a precise question, design methods, and analyze results.
• Provide structured feedback on drafts, figures, and presentations.
• Host short live sessions and share asynchronous comments to keep momentum.
  Deliverables are the student’s own work; mentors ensure rigor, ethics, and clarity.

• Typical load is ~1.5–2.5 hrs/week across 12 weeks:
• Live touchpoint: ~45–60 minutes (weekly or bi-weekly, depending on scope).
• Async feedback & prep: ~45–60 minutes (reviews, comments, planning).
  Schedules flex around exams/holidays; we keep scope sane to protect time.

• A 12-week roadmap with 10–12 live sessions plus weekly micro-milestones:
• Weeks 1–2: Topic, literature review, research question
• Weeks 2–4: Method, data plan, ethics, pilot
• Weeks 4–6: Data collection & analysis
• Weeks 6–10: Writing, figures, citations
• Weeks 10–12: Revisions, 10-min talk, submission package (journal/competition/poster)

Competitive stipends paid. Rates vary by track/scope and are paid in USD. We provide documentation for invoicing and basic tax guidance (mentors handle their own filings).

• Student-authored: No ghostwriting. Mentors coach structure, rigor, and ethics.
• Authorship: Mentors are acknowledged by default. Co-authorship is considered when mentors contribute substantial original scholarly work (pre-agreed and field-standard).
• Publication: The Unicorn Labs supports quality submissions; acceptance isn’t guaranteed and depends on venue fit and peer review.

Students own their original work. If mentors bring pre-existing code, datasets, or proprietary methods, those remain the mentor’s or third party’s IP (used under license/permission). Any shared artifacts must respect license terms. We can facilitate simple NDAs if needed.

After a 15-minute mentor-match consult, we shortlist mentors based on topic fit, timeline, timezone, and student goals. You can accept/decline matches. We avoid conflicts of interest and ensure transparent scope before kickoff.

Commonly Zoom/Meet, Google Docs/Drive, Notion (logs), Overleaf/Zotero (papers), and optional GitHub/Colab for code. We emphasize reproducible workflows (clean folders, versioned drafts, figure exports).

We follow a student-safety policy:

• Recordable video platforms, professional communication only.
• No off-platform direct messaging; parent/guardian copied on onboarding.
• Mentors avoid collecting personally identifiable or sensitive data unless pre-approved and anonymized.
• Report any concerns to The Unicorn Labs immediately; we’ll escalate per policy.

We step in with scope triage (reduce variables, shorten horizons, simplify methods). If needed, we reassign or add a specialist mentor. The goal is a finishable project with genuine learning and credible artifacts.

AI/ML (applied, interpretable models), computational biology, behavioral experiments, climate/policy analysis, data journalism/humanities DH. Interdisciplinary blends (e.g., remote sensing + policy, AI + ethics) are especially popular.

Yes—specific, evidence-based letters are welcome if you can speak to the student’s process and outputs (e.g., independence, methods, results, revision discipline). We discourage generic letters.

We request 48-hour notice for schedule changes. If a mentor becomes unavailable long-term, The Unicorn Labs coordinates a smooth handoff with shared notes and a recap session so momentum isn’t lost.

Submit a brief profile (field, methods, recent work, mentoring style) via our Mentor Application. We’ll schedule a short interview, verify credentials, and add you to our mentor network for matches.