This multi-year program will host several postdoctoral researchers working on either:

(a) foundational problems in machine learning, optimization, and statistics and their relationship to algorithmic and methodological improvements for training and deploying ML models or

(b) problems that advance the state of the art in central use-cases of large scale ML: video, imaging, and navigation or some combination of the above topics.

Descriptions of the scientific agendas of IFML and TRIPODS can be found at and respectively.

Fellows will be able to collaborate with numerous researchers and faculty involved in IFML partner institutions: the Machine Learning Lab at UT Austin, the University of Washington, Microsoft Research (Redmond), and Wichita State University. Fellows will play a leading role in organizing seminars, workshops and other research activities. The anticipated term for a fellowship is one or two years – to be decided at the time of appointment, with the possibility of extension based on mutual agreement. In addition to competitive salary and benefits, the fellowship also includes funding for independent travel to workshops, conferences and other universities and research labs.

Simultaneous applications for a joint Simons-UT ML Research Fellowship are possible! Please indicate a simultaneous application in your materials.

In order to apply, please email a CV, research statement, and have two reference letters sent to and Applications are due Dec. 15, 2020 and reference letters are due Dec. 31, 2020. Decisions will be made in February.

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