Post date: Wednesday, September 16, 2026 - 12:28

The Department of Mathematics and Statistics anticipates appointment of a full-time, tenure-track Assistant Professor in Data Science beginning Fall 2027. A PhD in Statistics, Mathematics, Computer Science, or a closely related field is required by the appointment start date. For best consideration, please submit all application materials by October 19, 2026.

The Department seeks candidates pursuing research in one or more areas central to modern Data Science, such as statistical learning, causal inference, experimental design, probability, stochastic PDEs, algebraic statistics, normalizing flows, scientific machine learning, optimization, inverse problems, or data assimilation, who have the potential to supervise PhD and master's student research with ability to contribute to curriculum needs in the department. The successful candidate will play a key role in developing and delivering core courses in our Data Science Concentration, which launched in Fall 2026, including MATH 2120 Introduction to Data Science and MATH 381 Mathematical Foundations of Data Science. The concentration integrates departmental strengths across Statistics (e.g., statistical learning, causal inference, experimental design), theoretical areas (e.g., probability, stochastic PDEs, algebraic statistics, topological data analysis), and computational areas (e.g., scientific machine learning, optimization, inverse problems, data assimilation). The position offers opportunities for cross-disciplinary collaboration across UNM and with national laboratories such as Sandia National Laboratories.

The minimum requirements are the following:

·        A PhD in Statistics, Mathematics, Computer Science, or a closely related field by the appointment start date, August 2027.

·        Research experience in one or more areas central to modern Data Science, such as statistical learning, causal inference, experimental design, probability, stochastic PDEs, algebraic statistics, normalizing flows, scientific machine learning, optimization, inverse problems, or data assimilation.

·        Teaching experience at the university level.

The preferred requirements are the following:

·        A proven record of, or clear potential for, impactful research in one or more of the following areas: statistical learning, causal inference, experimental design, probability, stochastic PDEs, algebraic statistics, normalizing flows, scientific machine learning, optimization, inverse problems, data assimilation.

·        Demonstrated excellence in teaching, including mentoring students at all levels.

·        Ability to teach and develop courses in the Data Science concentration and to contribute to broader departmental needs in statistics, applied mathematics, or pure mathematics.

·        Demonstrated or potential engagement in cross-disciplinary collaboration, including with other departments at UNM or with national laboratories such as Sandia National Laboratories.

·        Postdoctoral experience

·        A demonstrated commitment to cultivate an understanding of the rich and varied cultures of New Mexico and the success of the university's mission to serve local and global communities.

A complete application consists of the following:

·        Cover letter, including a single statement that addresses all of the preferred qualifications listed above

·        Curriculum vitae

·        Research statement

·        Teaching statement

·        A statement demonstrating commitment to student participation and success in the mathematical sciences, as well as working with broadly diverse communities

·        Four letters of recommendation, one of which addresses teaching

Only the application materials requested will be considered.

Review of applications proceeds in stages. Complete applications are screened against the minimum qualifications and then evaluated against the preferred qualifications. A group of semi-finalists is invited to a remote interview, and every semi-finalist receives the interview questions in advance. Finalists are invited to campus for a research presentation and meetings with faculty and students, and the references of finalists may be contacted by telephone. Applicants will be notified before their materials are shared with the departmental faculty.

To apply, visit the UNMJobs website: https://unm.csod.com/ux/ats/careersite/18/home/requisition/37719?c=unm&c...

Letters of recommendation should be sent by email to ahathawa@unm.edu or by surface mail to Department of Mathematics and Statistics, Data Science Search Committee, 1 University of New Mexico, MSC01 1115, Albuquerque, NM, 87131. For best consideration, completed applications should be received by October 19, 2026. Applications completed after that date will not be reviewed. Applications without four letters of recommendation will not be considered. The University of New Mexico is an EEO/AA Employer. All qualified applicants are encouraged to apply. The Applicant is required to provide official certification of successful completion of all degree requirements prior to her/his initial employment with UNM.

Albuquerque has a rich heritage and boasts a vibrant scientific environment that provides many opportunities for research collaborations and funding. UNM houses the Center for Advanced Research Computing, the Center for Quantum Information and Control, and the Center for High Technology Materials, among others, and it is near Sandia National Laboratories, Kirtland’s Air Force Research Laboratory, and Los Alamos National Laboratory. 

UNM is the premier research university in New Mexico, and is a Carnegie Highest Research Activity Institution and a federally designated Hispanic Serving Institution. New Mexico is a majority minority state. Our campus is located in the heart of Albuquerque, which has cultural, outdoor and recreational opportunities for everyone.  UNM is located on the traditional homelands of the Pueblo of Sandia.  The original peoples of New Mexico – Pueblo, Navajo, and Apache – and their connection to this land remains significant.  Learn more about our city, our welcoming campus, and research opportunities at http://advance.unm.edu/why-abq/ and  https://advance.unm.edu/why-unm/.