Faculty Positions – Artificial Intelligence Cluster

Job Summary

University of Texas at Arlington (UTA) is pleased to announce a cluster hiring initiative in artificial intelligence (AI). We seek to recruit a cluster of distinguished faculty members that will contribute toward strong AI, i.e., AI systems that are grounded in human‑curated domain knowledge such as natural laws, scientific theories, commonsense facts, and regulatory constraints. These systems have capabilities to explain their outputs by how they reason based on knowledge, that understand risks and ensure responsible actions that meet societal expectations, that are resistant to attacks and manipulation, and that adjust their behaviors by fusing human intelligence with machine intelligence.

The proposed cluster includes multiple open‑rank (tenured or tenure‑track) positions. By integrating diverse yet complementary specialties, this cluster hire will foster interdisciplinary collaboration, drive innovative research, and develop holistic approaches to AI challenges. This strategic assembly will not only enhance UTA’s research capabilities in multiple disciplines but also position it as a leader in creating trustworthy, responsible intelligent systems that can challenge human cognitive functions across various domains.

The ideal candidate will advance learning and knowledge through research, teaching, service, and commercialization in disciplines in our College of Business, College of Engineering, and the College of Science. Upon employment, each faculty member’s tenure home will be in one of these colleges or schools. The new faculty hires will bridge disciplines across the college/school and their departments, stimulating innovative research and teaching efforts.

Candidates are expected to demonstrate the ability to work effectively in a highly collaborative, engaging, and dynamic environment comprising individuals with various backgrounds, skills, and perspectives. We seek candidates with funded research programs who can produce research and scholarly or creative achievements that enhance UTA programs and disciplines. A strong promise of extramural grant funding will also be considered for Assistant Professor applicants.

New faculty will be expected to deliver high‑quality teaching using evidence‑based practices to engage students from various backgrounds and experiences effectively. The appointments are expected to commence September 1 2026, for the start of the Fall 2026 semester. Important Message:
All employees serve as a representative of the University and are expected to display respect, civility, professional courtesy, consideration of others, and discretion in all interactions with members of the UT Arlington community and the general public.

We are seeking candidates with expertise in Strong AI, as broadly described above. The following is a non‑exhaustive sample of research areas that align well with our interests.

• Neural‑symbolic AI:
  Symbolic AI operates on structured data, knowledge graphs, physics theorems, rules, and logic, enabling explicit reasoning and the manipulation of abstract concepts based on prior human knowledge. Neural‑symbolic AI integrates the learning capabilities of neural networks with the reasoning and representational abilities of symbolic AI, thereby enabling more complex reasoning and problem‑solving that meet real‑world expectations.
• Human‑in‑the‑loop Machine Learning:
  The ideal candidate will bring innovative approaches to designing interactive AI systems that leverage human intuition and expertise to guide the learning process, improve data annotation, and refine model decisions. The ideal candidate is also expected to explore the critical interplay between AI technologies and ethical considerations, particularly the moral, societal, and legal implications of AI, such as bias, privacy, accountability, and the impact of AI decisions on individuals and communities.
• Probabilistic Modeling Toward Strong AI:
  We are seeking candidates whose expertise is grounded in a sophisticated understanding of how probabilistic modeling plays a crucial role in knowledge‑informed machine learning. The ideal candidate will…