Tenure-track position in Mathematical Sciences – Computational Mathematics (AY27/28)
Description
**Description**
The Department of Mathematical Sciences at Carnegie Mellon University invites applications for an open rank tenure-track position to begin September 1, 2027. This search will focus on the area of computational mathematics broadly construed. This includes optimization, mathematical foundations of machine learning and AI, AI for mathematics, and numerical analysis.
Demonstrated experience in mentoring students is desirable, and interest in advancing interdisciplinary endeavors is valued. Through this appointment the Department hopes to enhance our existing connections with the School of Computer Science, the Department of Statistics and Data Science, the College of Engineering, the Tepper School of Business, the Hoskinson Center for Formal Mathematics, or to develop new connections.
**Qualifications**
A Ph.D. in Mathematics or a closely related discipline is required.
**Application Instructions**
Applicants should submit all materials electronically through Interfolio and MathJobs (https://www.mathjobs.org/jobs/list/28777) . This includes a cover letter, curriculum vita, list of publications, a statement describing current and planned research, and a teaching statement. Candidates should also arrange for at least three letters of recommendation that will be solicited through MathJobs.
The review applications will begin on November 15, 2026. However, applications may be accepted and reviewed until the position is filled.
**Equal Employment Opportunity Statement**
**Carnegie Mellon University** is an equal opportunity employer. It does not discriminate in admission, employment, or administration of its programs or activities on the basis of race, color, national origin, sex, disability, age, sexual orientation, gender identity, pregnancy or related condition, family status, marital status, parental status, religion, ancestry, veteran status, or genetic information. Furthermore, Carnegie Mellon University does not discriminate and is required not to discriminate in violation of federal, state, or local laws or executive orders.
