83 Sciences

AI-native materials discovery powered by unpublished experimental data

[Ian Naccarella @IanNaccarella]

Attending YC’s Startup School this weekend was a great reminder that we're building 83 Sciences at the right moment. Young talent is already pushing the boundaries of materials science by conducting research at top research labs. Almost every conversation surfaced the same pain point we saw firsthand: years of valuable experimental data being unfortunately difficult to search, reuse, and learn from, and frontier industries experiencing the physical bottleneck of innovative materials.

To scale, we're hiring a Founding AI Engineer to join us early. If building AI for science sounds interesting, reach out and connect with me at [email protected]

[Tyler Bosmeny @bosmeny]

This is cool. 83 Sciences is using AI to mine discarded lab data (like failed experiments) for scientific breakthroughs.

[83 Sciences @83sciences]

Bonus if you love science, have a background in chemistry, was a first-author or major contributor, and enjoy nerdy lab bench jokes

[83 Sciences @83sciences]

🙋‍♀️We're hiring our Founding AI Engineer at 83 Sciences!

What you'll do: help design insight and discovery extraction models and ship tools for scientists and work closely with the founders

[83 Sciences @83sciences]

Then apply here to join our team: https://t.co/ffqJ5jkqhI

[83 Sciences @83sciences]

The next industrial era will be built on materials discoveries and insights hiding in the 90% of experimental data that never gets published.

Innovate faster, publish more, do more with your data with 83 Sciences

https://t.co/uUnoLgZj3r