Tenure Track – Open Rank Faculty Position in Medical AI


  • Professional
  • Houston, Texas
  • Posted 1 month ago
  • Expires on: July 17, 2019

UTHealth School of Biomedical Informatics
Center for Cognitive Informatics and Decision Making

Tenure-Track, Open Rank Faculty Position in Medical AI

JOB SUMMARY: The School of Biomedical Informatics (SBMI) at the University of Texas Health Science Center at Houston (UTHealth) is recruiting one full-time, tenure-track faculty member, open rank, in applications of artificial intelligence (AI) and machine learning (ML) to medicine and healthcare.

The successful applicant will join a dynamic faculty at SBMI who are active in research, education, and applying informatics to medicine and healthcare. SBMI is one of the largest programs of biomedical informatics in the nation with about fifty regular faculty and additional fifty adjunct faculty. SBMI’s Vision is “Transforming Data to Power Human Health”. The Mission of SBMI is to collect, process, and convert data – ranging from molecules to populations – into actionable information, knowledge, and intelligence; educate current and future leaders, innovators, and problem solvers across Texas, the nation, and the world; disrupt, transform, and innovate to elicit biomedical discoveries, improve healthcare delivery, and aid in disease prevention by conducting outstanding basic and applied research and developing impactful information technology products and solutions.

SBMI offers one master’s degree with two tracks (research and applied) and two doctoral degrees (research-focused Doctor of Philosophy or PhD and practice-focused Doctor in Health Informatics or DHI), along with several graduate certificate programs, in the unique environment of the Texas Medical Center, the most concentrated area of biomedical and healthcare expertise, knowledge and skills in the world with more than fifty health-related institutions, 100k employees, 9,200 patient beds, and 10 million patient encounters, 180k surgeries, and 750k ER visits per year.

QUALIFICATIONS:  Applicants should possess a PhD in computer science, engineering, biostatistics, cognitive science, biomedical informatics, or related disciplines. Candidates possessing MD-PhD are highly encouraged to apply.  MDs with a strong quantitative background will also be considered. We are especially interested in applicants with strong background in AI and ML and an interest in integrating multiple sources of data (clinical, behavioral, temporal, environmental, etc.) to understand human behavior (both patients and providers) and its implications for health quality, safety, and efficiency.

Applicants at the associate or full professor levels should have a strong track record of teaching at the graduate level, extramural funding, and published research. Applicants at assistant professor level should have demonstrated potentials in extramural funding and published research and a strong commitment to graduate level teaching. Candidates who are likely to develop collaborative research with other SBMI faculty and faculty at UTHealth and the Texas Medical Center are preferred.

RESPONSIBILITIES: The successful candidate will be expected to conduct funded research, participate in teaching activities at the graduate level, and provide service at the school, university, national, and international levels. Collaborative research with other faculty in the school and across UTHealth and the Texas Medical Center is expected.

SALARY: Competitive and dependent upon qualifications and experience.

APPOINTMENT/BENEFITS:  This position is a 12-month full-time appointment on the tenure-track.  All interested parties should go to the link below to provide their curriculum vitae, research and teaching statement, names of three references, and a letter describing the applicant’s qualifications and career goals.

Link to job: http://p.rfer.us/UTH4Nd4s5

UTHealth is an EEO/AA employer.  UTHealth does not discriminate on the basis of race, color, religion, gender, sexual orientation, national origin, genetics, disability, age, or any other basis prohibited by law. EOE/M/D/F/V.