Open Rank TenuredTenure Track Professor of Data Science in Natural Language Processing

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 nations 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.

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 Universitys mission is essential for all candidates ( 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.