Tenured or Tenure-Track Faculty Position, Fall 2025 - Computational Neuroscience- UT Austin

The Oden Institute for Computational Engineering and Sciences and the Department of Neuroscience at The University of Texas at Austin have an opening for a tenured or tenure-track faculty position beginning Fall 2025 in the area of Computational Neuroscience. We are interested in candidates with expertise in the development and use of AI and machine learning approaches to solve questions in fundamental and translational neuroscience. Examples include analysis of large-scale, multimodal neural and behavioral recordings, biophysical modeling of molecular and cellular interactions across time scales, and use of digital twins for real time predictive analytics of behavior, circuit function, and molecular interactions. Expertise in fundamental and applied machine learning and artificial intelligence for gaining insights into neuropsychiatric disease states is of particular interest. This search is being conducted jointly between the Oden Institute and the Department of Neuroscience as one of several faculty lines committed to expanding the development of AI for Science and Scientific Machine Learning at UT Austin. The successful candidate will have half of their teaching duties in the Oden Institute's Computational Science, Engineering and Mathematics graduate program and half in the Department of Neuroscience. This position is open to applicants at all ranks: assistant, associate, and full professor, including the possibility of appointment as a chaired professor for candidates with exceptional qualifications. For more information about the Oden Institute for Computational Engineering and Sciences, please visit https://www.oden.utexas.edu. For more information on the Department of Neuroscience, please visit https://neuroscience.utexas.edu/.

Candidates must have a Ph.D. degree in neuroscience, molecular biology, biochemistry, engineering, biophysics, computational science or a related field. In addition to the ability to teach neuroscience classes at the undergraduate and graduate levels, the successful candidate must be qualified to teach graduate classes in computational science, engineering and mathematics. The successful candidate will be expected to create undergraduate and graduate learning environments that address the needs of students from a variety of backgrounds, with differing learning styles and abilities. Further, the successful applicant will be expected to develop an externally sponsored research program, mentor graduate students, collaborate with other faculty, and be involved in service to the university and profession.

Interested applicants should submit the following materials: (1) a cover letter, (2) curriculum vitae, (3) research statement (max four pages), (4) teaching and mentoring statement (max three pages), and (5) either three letters of reference (candidates for an assistant or associate professor position) or the names of three references (candidates for a full professor position).

Candidates should address their submission to the Computational Neuroscience Faculty Search Committee. Review of applications will begin November 11, 2024, and will continue until an appropriate candidate is identified. We anticipate virtual interviews in December and in-person campus interviews in January 2025.

The successful candidate will be required to complete an Employment Eligibility Verification form and provide documents to verify identity and eligibility to work in the USA. A security sensitive background check will be conducted on the applicant selected.

Please address questions to either co-chair of the Computational Neuroscience Faculty Search Committee: Dr. Robert O. Messing (romessing@austin.utexas.edu) or Dr. David Paydarfar (david.paydarfar@austin.utexas.edu). ** For application questions, contact Ruth Park (ruth.park@oden.utexas.edu).

The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.