The Computational Linguistics at Yale (CLAY) Lab, directed by Bob Frank, is seeking to appoint an NSF-funded postdoctoral researcher. The researcher will take part in a research project exploring the viability of deep neural networks as models of natural language learning. Specifically, the project aims at an understanding of the relationship between the inductive biases of neural networks and the constraints on syntax that generative linguists take to be imposed by universal grammar. The work will be conducted in collaboration with researchers at Yale University and Johns Hopkins University. The initial appointment will be for one year, with the opportunity of extension for a second year.
Applicants should have a publication record that includes computational modeling of language structure, processing or acquisition, as well as a background in syntax. Practical and theoretical computational skills are expected, but no specific expertise in neural network modeling is necessary. Candidates should, however, be willing to commit to quickly getting up to speed in the practice of neural network research. Finally, prior to beginning the position, applicants should have completed the requirements for their PhD in a relevant field (e.g., linguistics, computer science, psychology, or cognitive science).
To apply, submit via email the following materials: (i) a cover letter that briefly describes previous research accomplishments and areas of interests for future work, (ii) a current CV, and (iii) two representative pieces of research (links to on-line publications are sufficient). In the cover letter, please also include contact information for three references.
The position is available immediately and the start date is flexible. Applications will be considered on a rolling basis as they are received until the position is filled.
Yale University is an Affirmative Action/Equal Opportunity Employer, and we especially encourage applications from women and members of under-represented minorities.