Postdoctoral Fellow in the Theory of Representation Learning in Artificial and Natural Systems

October 15, 2021

We have a postdoc position to work on representation learning in brains and machines! See official ad below. To apply please follow the instructions here: .


Postdoctoral Fellow in the Theory of Representation Learning in Artificial and Natural Systems 

The Harvard Theory of Machine Learning invites applications for a postdoctoral fellowships in a multidisciplinary study of the underpinnings of deep learning, and in particular representation learning, transfer learning, generalization, and connections between artificial neural networks and natural learning systems including human and animal brains.  We are looking for exceptional junior scientists to work collaboratively with a group of faculty from Computer Science (Boaz Barak), Statistics (Lucas Janson), Electrical Engineering (Demba Ba) and Applied Mathematics (Cengiz Pehlevan) towards a theory of representations in artificial and natural systems. Candidates should suggest at least two of these faculty as potential advisors for their proposed research. The duration of the fellowship will be one year with the possibility of extending for an additional year. 

Candidates should have backgrounds in one or more of the following areas: machine learning, statistics, computational neuroscience, theoretical computer science, applied mathematics, or electrical engineering.  We do not expect candidates to know everything about all these areas, but be willing to learn and collaborate with researchers from different areas and backgrounds. 

The candidate will be expected to publish scholarly papers, attend internal, domestic, and international conferences and meetings, as well as take on a mentorship role for undergraduate and graduate students.

The Michael O. Rabin Postdoctoral Fellowship in Theoretical Computer Science, the Privacy Tools Project, Theory for Society, and Theory for Machine Learning are dedicated to building a diverse community that is welcoming for everyone, regardless of disability, gender identity and expression, physical appearance, race, religion, or sexual orientation.  We strongly encourage applications from members of underrepresented groups.