Our Mission


To usher in a new class of machine learning for scientific data, building models that can leverage shared concepts across disciplines. We aim to develop, train, and release such foundation models for use by researchers worldwide.

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Recent News


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The Well

Dec 03, 2024

We release The Well, a large-scale collection of physics numerical simulations created with domain experts and formatted for a machine learning usage.

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Multimodal Universe

Dec 03, 2024

100TB of cross-matched, standardized astronomy data that brings together images, spectra, and time-series data from leading surveys to accelerate machine learning breakthroughs.

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AstroCLIP Update

Jun 11, 2024

We release a significant update to the AstroCLIP model, which demonstrates superior performance on all previously tested downstream tasks and introduces the capacity to tackle a host of new problems.

The Team


Scientific Advisory Group


Colm-Cille
Caulfield

University of Cambridge

Leslie
Greengard

Flatiron Institute
New York University

David
Ha

Sakana AI

Yann
LeCun

Meta AI
New York University

Stephane
Mallat

École Normale Supérieure
Collège de France
Flatiron Institute

David
Spergel


Simons Foundation
 

Olga
Troyanskaya

Flatiron Institute
Princeton University

Laure
Zanna

New York University

Participating Institutions


Careers


Current job openings

Research Software Engineers In-person - Manhattan, NYC / Rolling Deadline

Research Software Engineers In-person - Manhattan, NYC / Rolling Deadline

Contact Us


Need more information? Get in touch.

info@polymathic-ai.org