The School of Information (iSchool) at The University of Texas at Austin (UT Austin) invites applicants for up to two tenure-track Assistant Professor positions to start in Fall 2025. Candidates will be expected to conduct innovative and impactful research and to teach at both graduate and undergraduate levels. As an interdisciplinary academic unit, our faculty are drawn from a wide range of different disciplines and collaborate broadly with other units on campus. We welcome applications from diverse disciplines including (but not limited to): information science, computer science, data science, electrical and computer engineering, human-centered computing, computational science, computational linguistics, and design. We seek strong scholars with compelling research agendas, regardless of disciplinary background.

We seek candidates who investigate human-centered artificial intelligence (AI) systems through designing, building, and technically evaluating such systems. Our call is intended to be broadly inclusive of the range of AI subdisciplines and areas such as (but not limited to): machine learning (ML), natural language processing (NLP), computer vision (CV), and generative AI and large language models (LLMs), etc. Candidates should develop AI systems in their research that advance support for human work or activities, e.g., by augmenting and amplifying the capabilities of individuals or groups of people.

Research directions in this area may include (but are not limited to):

• Innovative methods and applications that integrate AI with human computation
• Complementary human-AI teaming and supportive workflow design
• Human-in-the-loop decision-making and decision-support
• AI-assisted data annotation
• Accelerating and improving human-centered AI evaluation protocols
• Imagining other novel forms of human-AI partnerships

• Advancing fundamental understanding of the nature and range of human-AI partnerships, as well as how best to design, build, and evaluate them
• Investigating potential productivity benefits, such as the speed, scale, quality, and/or economics of human labor with vs. without AI-augmentation
• Advancing ethical and responsible design for system users, AI supply-chain workers, and/or society at-large around issues such as: trustworthiness and reliability; transparency and interpretability; fairness and social justice (for both AI users and workers); and accountability and algorithmic recourse
• Protecting private and sensitive data; the information environment and information integrity; human safety, health, and wellbeing; and the environment, via sustainable, green computing