Candidates are required to have received a Ph.D. degree or equivalent by the start of this appointment and may have up to three years of prior academic and/or postdoctoral research experience in areas related to data science and its mathematical foundations. Applicants should have strong research potential and a commitment to teaching. The position offers a competitive salary with funds for professional development.

The Department of Applied Mathematics and Statistics values diversity and inclusion as essential to achieving excellence. We especially encourage applications from those belonging to groups traditionally underrepresented in the mathematical sciences. Consideration of applications will begin on December 5, 2022, until the positions is filled.

Applicants should apply using http://apply.interfolio.com/114562

Submit the relevant materials online: a cover letter that identifies faculty members in the department with whom the applicant is interested in working, CV, a concise description of research interests and goals, a teaching statement that includes a discussion of the applicant's experience with or ideas for teaching in a diverse and inclusive classroom and three letters of recommendation, with at least one letter addressing the applicant's teaching abilities. Applicants may suggest any faculty members of the Whiting School of Engineering as potential mentors.

Equal Employment Opportunity Statement

The Johns Hopkins University is committed to equal opportunity for its faculty, staff, and students. To that end, the university does not discriminate on the basis of sex, gender, marital status, pregnancy, race, color, ethnicity, national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status or other legally protected characteristic. The university is committed to providing qualified individuals access to all academic and employment programs, benefits and activities on the basis of demonstrated ability, performance and merit without regard to personal factors that are irrelevant to the program involved.