UmFsZXdheTpyZWd1bGFy
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
The Machine Learning in the Chemical Sciences and Engineering from the The Camille & Henry Dreyfus Foundation is a multi-purpose grant to support the adoption of machine learning / data science approaches in chemical sciences and engineering. The funding may be used for, but not limited to, education, research, postdoctoral training, or scientific exchange. Some examples include:
- molecular synthesis, including mechanisms, techniques, and applications
- theory, computation, physical properties of molecules or materials
- rates and mechanisms of new chemical processes
- new or improved materials and materials applications
- postdoctoral support for collaborations that combine chemical science research with ML expertise
- collaborative sabbaticals, extended visits, and meetings
- education, e.g., new courses, seminar series, MOOCs,…
- public libraries of chemistry and chemical engineering data for use in ML
Applicants must be based at academic institutions in the USA that grant a bachelor’s or higher degree in the chemical sciences. Funding is proposal-dependent and ranges from 50,000 – 150,000 USD. Applications close on April 6, 2023.