WASHINGTON UNIVERSITY IN ST. LOUIS. The Department of English seeks applications for a tenure-track assistant professorship in English, American, or Anglophone literature, with literary subfield open, to begin in the fall semester of 2016; the candidate should have expertise in computational approaches to literary analysis and will be expected to make a sustained contribution to the university’s interdisciplinary initiative in Data Science. Duties will include teaching assigned courses, conducting research, writing for publication, advising students, participating in department governance, and university service, especially in advancing the research mission of the Humanities Digital Workshop. We’re especially interested in scholars with expertise in the use of quantitative methods, for stylometrics, for the analysis of large corpora, and for the study of book history. Although the candidate’s home department will be English, at least half of his or her teaching will be devoted to interdisciplinary teaching in the new curriculum in data science for the humanities, with undergraduates and graduate students from departments of language and literature, Comparative Literature, History, and, possibly, Linguistics, Music, Art History, and Computer Science. Aptitude and, if possible, experience in collaboration and mentoring are important. Appointment requires a PhD in literary study in hand by July 1, 2016.
Applicants should submit a cover letter addressed to Joseph Loewenstein, Chair of the Recruiting Committee, a c.v. and a 25-page writing sample, and arrange for the submission of 3 letters of recommendation through Interfolio. Full consideration and priority will be given to application materials received by January 4, 2016.
Washington University in St. Louis is committed to the principles and practices of equal employment opportunity and affirmative action. It is the University’s policy to recruit, hire, train, and promote persons in all job titles without regard to race, color, age, religion, gender, sexual orientation, gender identity or expression, national origin, veteran status, disability, or genetic information.