As part of a major new initiative in interdisciplinary data
science, Princeton University is searching for tenured and
tenure-track faculty members across all science, engineering,
social science, and humanities areas. This initiative will involve
multiple faculty hires over the next several years. We are
particularly interested in applicants who advance discovery in
their fields of scholarship using techniques from machine learning
and statistics.
Applicants may also make research advances in machine learning and
statistical methods as necessary for their application domains.
These faculty hires will contribute to the momentum building across
Princeton University in interdisciplinary data science. For
associate and full professor candidates, we seek research leaders
who cross boundaries in applying data science methods. For
assistant professor candidates, we are looking for rising stars
conducting exciting research that applies data science methods in
their chosen field(s). Applicants must demonstrate the potential
for superior research, scholarship, and teaching.
Faculty appointments resulting from this search may be made with
various departments, centers, or institutes at Princeton
University. A PhD is expected. In addition, applicants must have a
strong record of research productivity, demonstrate the ability to
develop a rigorous research program, and be dedicated to teaching
at both the undergraduate and graduate levels. The university is
committed to fostering a diverse and inclusive academic community.
We welcome applications from members of groups historically
underrepresented in their chosen fields.
Applications must be submitted online at https://www.princeton.edu/acad-positions/position/32067
and should include a cover letter, curriculum vitae, a research
statement, a teaching statement, and contact information for at
least three, but no more than five references. Review of
applications will begin by December 1, 2024, and applications will
be considered throughout the academic year. Princeton University is
an equal-opportunity employer. All qualified applicants will
receive consideration for employment without regard to race, color,
religion, sex, national origin, disability status, protected
veteran status, or any other characteristic protected by law. This
position is subject to the University's background check policy. recblid oy825ppfsqr4s25y957cyzzj4ufp42