Lecturer in Computational Science and Data Science
The Harvard John A. Paulson School of Engineering and Applied Sciences seeks applicants for the position of Lecturer in Computational Science and Data Science in the Institute for Applied Computational Science (IACS) (http://iacs.seas.harvard.edu), with an expected start date of July 1, 2020. This is an annual (twelve month) academic appointment renewable for up to three years, depending on continuing curricular need and performance.
Duties include teaching three computational or data science courses per year, supervising and advising master’s student projects, and conducting independent research in an area of the lecturer’s choosing. In addition, the lecturer will be expected to participate in the planning and execution of community events planned by the IACS.
Candidates are required to have a doctoral degree in Computer Science, Applied Mathematics, Statistics, or a related field (physical science or social science with a focus on computational methods) by the expected start date. In addition, we seek candidates with programming skills in some programming language and a record of teaching at the undergraduate or graduate level.
Required documents include: a cover letter, including a description of teaching experience and philosophy and comments on any efforts to encourage diversity, inclusion, and belonging; a current CV; and course evaluations for all recently taught courses. Candidates are also required to submit the names and contact information for three to five references. Three letters of recommendation are required, and the application is considered complete only when at least three letters have been received. Applicants can apply using this link: https://academicpositions.harvard.edu/postings/9228. We encourage candidates to apply by December 15, 2019, but will continue to review applications until the position is filled.
Harvard University is 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.