The Collaboration on the Theoretical Foundations of Deep Learning is a team of eleven research leaders from eight institutions around the world, sponsored by NSF and Simons Foundation, with the aim of addressing theoretical challenges of deep learning, extending its applicability, and developing new methods.
We are looking for postdoctoral team members who will collaborate with one or more of the following researchers:
Emmanuel Abbe (EPFL Lausanne), Peter Bartlett (UC Berkeley), Mikhail Belkin (UC San Diego), Amit Daniely (Hebrew University of Jerusalem), Andrea Montanari (Stanford University), Elchanan Mossel (MIT), Alexander Rakhlin (MIT), Nathan Srebro (Toyota Technological Institute at Chicago), Nike Sun (MIT), Roman Vershynin (UC Irvine), Bin Yu (UC Berkeley).
We expect that there will be up to six positions open each year. These positions emphasize strong mentorship, flexibility, and breadth of collaboration opportunities with other team members -- senior and junior faculty, postdocs, and graduate students at various nodes around the world.
Each position is a full-time appointment, and the start date is flexible. Candidates are encouraged to apply to work with more than one faculty mentor at one or more nodes. They should have an excellent theoretical background and a doctorate in a related field, including Mathematics, Statistics, Computer Science, and Electrical Engineering. We particularly encourage applications from women and minority candidates.
Simultaneous applications for a Simons-Berkeley Research Fellowship are possible; in that case, please indicate in your cover letter that you are also applying to participate in a program at the Simons Institute for the Theory of Computing.
The review process starts November 1, 2020 and will continue until positions are filled.
Please submit applications using this form: deepfoundations.ai/postdoctoral-applications-2020.