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

University of Virginia•Charlottesville, VA

1d•Onsite

About The Position

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.

Requirements

Earned, or 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.
Deep technical expertise in advancing language models.
Intellectual depth, original contributions, and a compelling long-term vision for advancing NLP and language model research.
A strong publication record in leading peer-reviewed NLP, computational linguistics, and AI venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, TACL, Computational Linguistics, or comparably selective venues).
For senior ranks: Demonstrated record of excellence in research, teaching, and advising; established national/international reputation.
For assistant rank: Demonstrated potential for excellence in methodological development and scientific impact; prior experience in educational-related activities.

Nice To Haves

Contributions that improve the understanding or capabilities of language models.
Connecting language model research with other areas of data science.
Connecting language model research with important domains across the University.
Research in areas such as: Foundations, training, and efficiency of language models (architectures, learning objectives, data curation, pretraining and post-training, scaling, long-context modeling and memory, continual learning, efficient training and inference).
Research in areas such as: Reasoning, knowledge, and agentic AI (reasoning and planning, tool use, retrieval-augmented and knowledge-grounded generation, symbolic methods, autonomous and multi-agent systems, human-agent collaboration).
Research in areas such as: Multimodal and grounded language intelligence (vision-language, speech- and audio-language, video-language, cross-modal learning, world models).
Research in areas such as: Multilingual and human-centered NLP (low-resource methods, language diversity, linguistic and cognitive foundations, dialogue and interactive systems, accessible and inclusive language technologies).
Research in areas such as: Language models integrated with data science and domain discovery (connecting language with structured, temporal, scientific, or multimodal data in areas such as science, engineering, health, education, social sciences, and public policy).

Responsibilities

Conducting foundational and methodological research in Natural Language Processing (NLP) and Large Language Models (LLMs).
Advancing language models, including their architectures, learning objectives, data and training methods, adaptation and post-training, reasoning, evaluation, and efficient implementation.
Connecting language model research with other areas of data science and important domains across the University.
Contributing to a collaborative faculty community committed to research excellence, innovative teaching, interdisciplinary partnership, and the responsible advancement of data science and AI.
Shaping a rapidly evolving field.
Leveraging the strengths of a leading public research university for interdisciplinary collaboration and scholarly impact.
Detailing research expertise and interests, instructional experience, preferred teaching domain, and other scholarly interests in application.
Demonstrating a strong publication record in leading peer-reviewed NLP, computational linguistics, and AI venues.
For senior ranks: demonstrating a record of excellence in research, teaching, and advising, and establishing a national/international reputation.
For assistant rank: demonstrating potential for excellence in methodological development and scientific impact, and having prior experience in educational-related activities.