Skip to main content
GDC Sculpture

Postdoctoral Positions now Available for 2024

The NSF AI Institute for Foundations of Machine Learning (IFML) seeks highly qualified candidates (within five years of the award of their PhD) for a new UT ML Research Fellow Program. Appointments will begin Summer or Fall 2024.

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 or

(c) deep learning and protein biologics, especially protein engineering and applications of large-scale tools such as AlphaFold (we encourage candidates with PhDs in biology, chemistry, biochemistry, or related fields with a background in computation to apply).

Descriptions of the scientific agenda of IFML can be found at

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.

Submission requirements: a CV, research statement, and two reference letters. Applications will be accepted and reviewed on a rolling basis.

Apply here: