Life; Machine Learning; and the Search for Habitability

This effort will produce a robust machine learning (ML) model capable of accurately predicting the habitability of exoplanets. This proposed ML model will target the enhancement of the Habitable Worlds Observatory (HWO) mission concept in many ways; including; but not limited to; prioritizing exploration of exoplanets; the discovery of novel habitability indicators; and onboard data processing and autonomous science.

This study will directly impact the Habitable Worlds Observatory (HWO) mission by saving significant time; cost; and aid in driving scientific objectives for the mission. Employing an ML model for habitability prediction enables the processing of vast amounts of data at scale and at a small fraction of the time it would take for scientists to manually analyze each exoplanet; saving significant time and resources; and enabling scientists to explore a larger number of potential habitable candidates; thus expanding the scope of discovery; and fostering greater opportunities for groundbreaking scientific exploration.

This is early/mid-stage (TRL 2) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

None of these are guaranteed paths for this specific project — TechPort itself doesn't have an "apply" button. Reaching out to the contact(s) above with a specific question is usually the fastest way to find out what's actually open.