Harvard University Postdoctoral Fellow in the Study of Historical Psychology in Latin Texts in Cambridge, Massachusetts
Title Postdoctoral Fellow in the Study of Historical Psychology in Latin Texts
School Faculty of Arts and Sciences
Department/Area Human Evolutionary Biology
We seek a postdoctoral fellow with research experience in computational approaches to language and literature to work on a cross-institutional project on the study of historical psychology in Latin texts. This is a one-year full-time position beginning in Spring 2023 and potentially renewable for one additional year. Besides regular meetings with the project PI, Joe Henrich (Harvard University; Culture, Cognition, and Coevolution Lab), and co-PI, Jonathan Schulz (George Mason University), the position will entail close collaboration with the Quantitative Criticism Lab, co-directed by Joseph Dexter (Harvard University) and Pramit Chaudhuri (University of Texas at Austin). The aim of the project is to develop computational methods for the study of diachronic changes in psychology based on current research in the social sciences and recent advances in computational text analysis for Latin and other pre-modern languages. The work forms part of a larger multi-institutional project, funded by the Templeton Foundation, entitled “Religion, Family Structure and the Origins of Individual Freedom and Economic Prosperity.”
The successful applicant will join a cross-disciplinary, highly collaborative team of humanists, social/life scientists, and data scientists; this position will contribute to the team through expertise in natural language processing, corpus linguistics, computational literary studies, digital humanities, or a related area. Experience in Latin language and literature is desirable, but interested applicants who have worked on other literary traditions, or on text mining of large English corpora, are strongly encouraged to apply and will receive full consideration. Other desired but optional areas of experience include one or more of the following: cultural analytics, cultural evolution, history of ideas, and lexicography. The fellow will have no teaching responsibilities.
A Ph.D. in a computational, statistical, linguistic, or literary field is required; possible disciplines include (but are not limited to) anthropology, applied mathematics, bioinformatics, classics, cognitive science, comparative literature, computer science, English, evolutionary biology, information science, linguistics, psychology, and statistics. By the start date of the position, applicants should either have the Ph.D. in hand or be able to provide certification from their home institution that all degree requirements have been fulfilled. The Culture, Cognition, and Coevolution Lab space is based at Harvard University; residence near Cambridge during the fellowship period is preferred but not required.
Applicants should submit the following materials by February 15, 2023:
Cover letter describing their interest in the position and any relevant prior work;
Short (1-3 page) summary of past and current research interests, which gives particular attention to any computational work and includes links to a GitHub page or other online coding portfolio, if available;
Writing sample of not more than 30 pages;
Names and contact information of three referees (letters will be requested only for short-listed candidates).
Questions regarding the position may be directed to email@example.com. Given the interdisciplinary nature of the position, interested applicants are encouraged to discuss their interests and qualifications with the project team before applying.
Contact Email firstname.lastname@example.org
Equal Opportunity Employer
We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.
Minimum Number of References Required 3
Maximum Number of References Allowed 5