The Center for Data Science at New York University (NYU) invites applications for research scientists as part of the Polymathic AI initiative

Polymathic AI  is inviting applications for researchers in our team. Our research focuses on building and understanding foundation models for science, and in particular modeling complex interconnected systems from astronomy, biology, fluid dynamics to solar physics. For this position, we are looking for researchers that will contribute to our research on interpretability, generalization and general understanding of foundation models. The overarching objective is to create open access tools that enable researchers to reach meaningful understanding of the complex model quickly thus speeding up the pace of scientific discovery.

Fulfilling this objective requires creativity to combine core-principles of machine learning to the practical tools of deep learning, now available through modern foundation models. For the theory part, the selected candidate will work in close collaboration with the Adaptive Bayesian Intelligence group headed by Dr. Emtiyaz Khan at RIKEN-AIP (in Tokyo, Japan) and TU Darmstadt (in Germany). The candidate is expected to make the best use of adaptive-learning methods to develop practical tools to the most pressing scientific challenges in science. The collaboration may require some amount of travelling to Japan and Germany. 

The selected candidates will join a vibrant, interdisciplinary team based in New York, spanning NYU and the Flatiron Institute, composed of machine learning researchers, engineers, and domain scientists. This collaborative environment  at Polymathic AI offers a unique opportunity to work on cutting edge AI models and advance AI for scientific discovery.

Several positions are available. The review of applications will begin immediately and will continue until the positions are filled.

In compliance with NYC's Pay Transparency Act, the annual base salary range for this position is $80,000-$200,000.

We are seeking highly motivated individuals with backgrounds in computer science, machine learning, and science to join our group in a quest to perform breakthrough research. An ideal candidate has strong research skills in relevant areas: machine learning, foundation models, computational sciences. A PhD degree is required. Significant experience with programming, project management, and large-scale model training is a plus.

To apply, please send us your application and reference letters:

• The application should include:

• A 1-2 page research statement, please make sure to address the following:

• the specific areas you want to work on,

• how they fit with research in Polymathic,

• how they build upon your previous work, and

• your preferred start date;

• Your current CV with research background and a list of your publications;

• Copies of your two representative publications.

• Submit the application to LINK. We encourage you to also include links to any software that you have developed.

• Arrange for two letters of reference from your previous research supervisors and collaborators to be sent directly within 2 weeks of your submission.

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