Exploring the future of biology through computation.
Join a growing community of students working on real problems
in synthetic biology, neuroscience, and machine learning.
Biology meets AI. Built for high school students.
The Neural Biology Project runs the NeuroBio Challenge, a multi-week online research and coding competition where students solve real biological problems using computational tools. You get curated prompts and datasets, beginner resources, and a clear submission format: a GitHub repository plus a short summary. Work solo or in a small team, on your own schedule.
Work on curated prompts tied to real biological domains like genetics, neuroscience, and biomedical data analysis. You won't be guessing what to do. You'll have a clear goal and a real dataset.
- Clean, competition-ready datasets
- Problem statements with success criteria
- Optional extensions for advanced students
If you're new, you won't get left behind. We provide beginner-friendly resources, examples, and guidance so you can learn while building something real.
- Python + data analysis starter materials
- Intro ML workflows and evaluation basics
- How to write a clean README and results summary
Everything you do ends in a GitHub repo you can show. You'll practice reproducibility, documentation, and clear presentation, which is what real research and real engineering demand.
- Readable code structure and documentation
- Results, plots, and brief conclusions
- Optional writeup template for students
- 1) RegisterCreate an account and join the current season.
- 2) Choose a promptPick a challenge prompt that matches your skill level and interests.
- 3) Build for 7-8 weeksWork asynchronously. Train a model, analyze data, and document your results.
- 4) SubmitTurn in a GitHub repo containing code, README, and results plus a short summary.
- 5) ReviewJudges score projects based on reasoning, implementation, clarity, and effort.
The NeuroBio Challenge is a student-led competition where high schoolers explore real-world problems at the intersection of biology and artificial intelligence. You might build a model to classify cell states, predict gene expression patterns, analyze neural signals, or extract insight from biomedical time-series. It's designed to feel like a real computational biology sprint: define the question, build a baseline, improve it, and explain what you found.
Your repo should run end-to-end. Use clear folder structure, a requirements file, and instructions for reproducing results.
- README with setup + how to run
- Requirements / environment instructions
- Organized code (not one giant notebook)
We're not just looking for “it trained.” Show metrics, plots, and comparisons to a baseline.
- Train/validation strategy
- Metrics and short interpretation
- Baseline vs improved approach
A brief, readable explanation: the question, the approach, and what you learned. Keep it clear and honest.
- Problem statement in your own words
- Approach and key design choices
- What worked, what didn't, what's next
Bring the challenge to your school with a local chapter.
Help fund prizes, compute credits, and free workshops for students.