Open Rank Tenured/Tenure Track Professor of Data Science in Natural Language Processing

Candidates must have earned, or be on track to earn, a PhD in Data Science, Computer Science, Computational Linguistics, Linguistics, Information Science, Statistics, Electrical or Computer Engineering, or a closely related field by August 2027 or appointment start date. A commitment to advancing the University’s mission is essential for all candidates ( https://provost.virginia.edu/faculty-handbook/mission-statement-university-virginia ).

When applying, candidates should detail their research expertise and interests, their instructional experience, preferred teaching domain, and other scholarly interests. Candidates should have a strong publication record in leading peer-reviewed NLP, computational linguistics, and AI venues. Examples include ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, TACL, Computational Linguistics, as well as other comparably selective venues appropriate to the work. Candidates for senior ranks (associate and full with tenure) must have a demonstrated record of excellence in research, teaching, and advising, in data science and/or closely related fields, and must have established a national/international reputation for contribution to the field in methodology, application, and impact. Candidates for assistant rank (tenure-track) must demonstrate the potential for excellence in methodological development and scientific impact in data science or a related field and have prior experience in educational-related activities.

The University of Virginia School of Data Science is seeking exceptional candidates for an open-rank tenured or tenure-track faculty position in Natural Language Processing (NLP), with particular emphasis on Large Language Models (LLMs). This search prioritizes faculty making foundational and methodological contributions that improve the understanding or capabilities of language models. We seek a scholar with deep technical expertise in advancing language models, including their architectures, learning objectives, data and training methods, adaptation and post-training, reasoning, evaluation, and efficient implementation. We especially welcome candidates who connect language model research with other areas of data science and with important domains across the University. A successful candidate will join a collaborative faculty community committed to research excellence, innovative teaching, interdisciplinary partnership, and the responsible advancement of data science and AI. Faculty have the opportunity to shape a rapidly evolving field while leveraging the strengths of one of the nation’s leading public research universities, with exceptional opportunities for interdisciplinary collaboration and scholarly impact.

We welcome candidates whose scholarship advances NLP and language modeling through foundational and methodological research. Areas of interest include, but are not limited to:

• Foundations, training, and efficiency of language models, including architectures, learning objectives, data curation, pretraining and post-training, scaling, long-context modeling and memory, continual learning, and efficient training and inference.

• Reasoning, knowledge, and agentic AI, including reasoning and planning, tool use, retrieval-augmented and knowledge-grounded generation, symbolic methods, autonomous and multi-agent systems, and human-agent collaboration.

• Multimodal and grounded language intelligence, including vision-language, speech- and audio-language, video-language, cross-modal learning, and world models.

• Multilingual and human-centered NLP, including low-resource methods, language diversity, linguistic and cognitive foundations, dialogue and interactive systems, and accessible and inclusive language technologies.

• Language models integrated with data science and domain discovery, including methods that connect language with structured, temporal, scientific, or multimodal data in areas such as science, engineering, health, education, social sciences, and public policy.

These areas are illustrative, and candidates are not expected to work across all of them. We are most interested in applicants with intellectual depth, original contributions, and a compelling long-term vision for advancing NLP and language model research., * A teaching statement that describes your teaching and mentoring experiences, and how they align with the UVA’s mission statement ( https://provost.virginia.edu/faculty-handbook/mission-statement-university-virginia ). This statement should focus on your past or planned educational activities and how you see data science education evolving over the next decade (1-3 pages).

• Three reference letters or detailed contact information for three references.

• If available, reviews & course evaluations from up to 3 educational courses, seminars, and/or short courses.

Review of applicants will begin on or around December 1st, 2026 , and the positions will remain open until filled. Rank will be commensurate with academic and industry experience. Appointments are available on 9-month contracts.

About the School

Founded in 2019, the School of Data Science at the University of Virginia (UVA) - the first of its kind in the nation - advances discovery, innovation, and societal impact through collaborative, open, and responsible data science research and education. The School brings together expertise across business, computation, engineering, humanities, law, mathematics, social sciences, statistics, and law to address complex, real world challenges. Its academic offerings include a B.S. in Data Science, an undergraduate minor, residential and online M.S. in Data Science programs, and a Ph.D. in Data Science, all designed to prepare students for a rapidly evolving data driven world. UVA researchers have access to the Afton and Rivanna high-performance computing systems, which provide tens of thousands of CPU cores, more than eight petabytes of storage, and NVIDIA A100, H200, and B200 GPUs. SDS also acquired additional H200 and B200 clusters to support its faculty’s research and education.