Center for Statistics and Machine Learning
Call for Azure Cloud Computing Proposals
With the support of a gift from Microsoft Corporation, the Center for Statistics and Machine Learning (CSML) is pleased to announce a call for proposals for Azure cloud computing grants and mini-grants. The grants are available to both faculty and students and can support computational data science research across a variety of domain areas, e.g. health and biomedical, life sciences, social sciences, chemistry, materials research, machine learning, etc.
We invite proposals based on a diversity of intellectual approaches and personal backgrounds.
A total of $25,000 of Azure cloud computing credits is available, with a maximum award of $10,000 per grant. Each grant takes the form of credits for the use Azure cloud computing resources. Credits must be used before the end of the spring term (June 1, 2021).
Proposal Preparation and Submission
Princeton faculty and students from all divisions are invited to apply. An applicant can submit on at most one proposal. Submission deadline: Friday, February 26, 20201, 9am.
Proposals should be concise; a paragraph or two is requested. Clearly specify the research objectives and outline the need for the computing resources. We expect that grants for one student will have a maximum budget of $5,000. Grants for faculty or groups should have a maximum budget of $10,000.
Proposals should be submitted via email to email@example.com
Local assistance in moving your proposed computing to Azure cloud computing will be available for funded proposals. Online training in Azure cloud computing resources are available at https://docs.microsoft.com/en-us/learn/ and https://github.com/Microsoft/computerscience .
Contact CSML Assistant Director, Sarah McGovern (firstname.lastname@example.org), with questions about proposal preparation and submission.
The Center for Statistics and Machine Learning collaborates on research and teaching that combines insights from computation, machine learning, statistics and the application of these ideas to specific application domains. To learn more, visit the CSML website at https://csml.princeton.edu/