CMU: Assistant Professor in Applied Statistical Machine Learning

CMU is seeking both tenure-track and research-track candidates with strong training in statistical machine learning and a demonstrated commitment to bringing methodological innovation to application-driven research.

Carnegie Mellon University At: Carnegie Mellon University
Location: Pittsburgh, PA

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The H.J. Heinz III College of Carnegie Mellon University is seeking both tenure-track and research-track candidates at the assistant professor level with strong training in statistical machine learning and a demonstrated commitment to bringing methodological innovation to application-driven research.

Successful candidates will have strong interests in research and teaching which complement the research concentrations, activities, and graduate education programs at Heinz College and demonstrated expertise with unstructured text data, spatial data, graph/network data, or other data types.

Examples of key research areas include health care, consumer behavior, and smart cities (e.g., transportation, crime, and public health). Heinz College faculty have made recent advances in these areas through methodological innovations in fields such as machine learning, statistics, econometrics, operations research, network analysis, and pattern detection.

Within Heinz College, labs and centers such as the Living Analytics Research Center (, Center for the Future of Work (, Event and Pattern Detection Laboratory (, Center for Health Analytics, and iLab ( provide considerable opportunities for working with societal scale data sets and industry partners.

Applicants are expected to demonstrate outstanding academic and research credentials, and to have completed or be nearing completion of a doctoral degree. PhDs in machine learning, statistics, computer science, or related fields are encouraged to apply. CMU's Heinz College consists of a multi-disciplinary faculty and graduate students in the School of Information Systems and Management and the School of Public Policy and Management.

Heinz College seeks to continue to recruit and retain a diverse faculty as a reflection of our commitment to the excellence of the College. We highly value our collaborative research, innovation, and learning community. We welcome applicants who have a demonstrated commitment to supporting the diversity of our faculty and graduate student populations through, among other things, mentoring female and under-represented minority students. Carnegie Mellon University strongly supports and enables interdisciplinary research collaborations across departments and colleges.

Many Heinz College faculty members are actively involved in collaborations with CMU's School of Computer Science, Department of Statistics, and other departments across the university, as well as advising students in our joint Ph.D. programs with the Machine Learning Department and Department of Statistics. Initial funding for the research-track position will be provided by the Living Analytics Research Center (, and it is desirable for candidates for this position to have a strong research interest in smart cities. We expect that this will be a long-term position and that continued funding will be obtained from other sources.

Research-track faculty at CMU's Heinz College share the privileges and responsibilities of tenure-track faculty, including full participation in promotion decisions, advising PhD students, and being PI on research projects (with no restrictions). They may and often do teach graduate-level courses for the College's various degree programs.

Applications must include a cover letter, curriculum vitae, statements of research and teaching interests, 2-3 representative papers, and the names of three or more individuals who have been asked to provide letters of reference. In your cover letter, please state whether you are applying for the research-track position, tenure-track position, or both positions.
Review of applications will begin December 1st.

Carnegie Mellon considers applicants for employment without regard to, and does not discriminate on the basis of, gender, race, protected veteran status, disability, or any other legally protected status.