Open Rank (Assistant/Associate/Full-Professor)

About the Role

The University of Texas at Arlington (UTA) is seeking three tenured and tenure-track faculty members for a cluster hire initiative focused on advancing research in large-scale precision medicine, clinical AI, and genomics. This initiative is a core component of UTA’s RISE 100 initiative and its strategic expansion in Precision Health and translational health sciences. UTA is establishing a leading program in health-system-embedded clinical informatics and precision health research.

Key Responsibilities

• Design, adapt, deploy, and evaluate digital pathways, workflow-integrated tools, secure data pipelines, and EHR-connected solutions in real healthcare environments.
• Conduct research within or adjacent to large-scale clinical platforms such as Epic.
• Develop or study EHR-integrated workflows, clinical decision support, phenotype-driven pathways, secure orchestration layers, implementation frameworks, and outcome-focused deployment models.
• Build and lead independent, externally funded research programs.
• Contribute to the academic mission, including research, teaching, mentorship of graduate students and clinical trainees, and service.
• Teach undergraduate and/or graduate courses.
• Build and lead a team of student researchers.
• Implement a program of externally funded research yielding top-tier publications.
• Contribute to professional service within UTA and the external community.

Areas of Interest

Research at one or more points along the following translational continuum is of particular interest:

• EHR-integrated digital pathway design and evaluation
• Clinical decision support and workflow-aware informatics
• Secure clinical data infrastructure and governed research environments
• Clinical NLP, language technologies, and communication tools embedded in care delivery
• Real-world implementation, monitoring, safety, and continuous improvement of healthcare AI and digital tools
• Epic-connected genomics, phenotype-driven precision medicine, longitudinal registries, and genome-informed care pathways
• Methods for conducting scalable clinical, quality, and implementation research in secure enterprise health IT environments

Requirements & Tech Stack

• Comfort operating in governed enterprise clinical environments.
• View the electronic health record (EHR) as a platform for translational research, clinical workflow innovation, and learning health system improvement.
• Experience with or adjacent to large-scale clinical platforms such as Epic.

Qualifications

• Ph.D. or equivalent in a relevant discipline aligned with Precision Health.
• Strong publication record or potential in the field of expertise.
• Strong research program with existing external funding or potential for funding.
• Commitment to quality teaching at the graduate and undergraduate levels.
• Proven record of impactful, productive research.
• Commitment to translating research into real-world clinical care.
• Ability to generalize and scale approaches, methods, or platforms across multiple diseases and clinical contexts.
• Demonstrated ability to bridge the basic, translational, and clinical continuum.
• Strong history of support from NIH, NSF, ARPA-H, PCORI, and industry sponsors.
• Scientific leadership through high-impact publications.
• Invited presentations.
• Track record of mentoring Ph.D. students, postdoctoral scientists, and junior faculty.
• Proven experience in multidisciplinary team leadership.
• Proven experience in real-world clinical or EHR-based environments.

Preferred Qualifications

• Internationally recognized or rapidly emerging research program in precision medicine, clinical AI, genomics, or biomedical informatics.
• Strong record of sustained extramural funding (e.g., NIH, NSF, ARPA-H, PCORI, foundations, or industry).
• Demonstrated leadership of interdisciplinary research programs.
• Mentorship of graduate students and postdoctoral scholars.
• Evidence of translating computational, genomic, or AI innovations into real-world clinical or healthcare settings.