Full Professor

Florida Atlantic University•Boca Raton, FL

6d•Onsite

About The Position

The Florida Atlantic University Charles E. Schmidt College of Medicine’s, Department of Population Health, is recruiting a visionary faculty member in Artificial Intelligence in Medicine to develop and lead transformative AI-driven initiatives that advance population health, health equity, healthcare delivery, biomedical and community-engaged research. This role offers the opportunity to advance AI applications, machine learning, data science and predictive analytics to address complex public health challenges, improve health outcomes, optimize healthcare systems, enhance biomedical research, innovate clinical research, and develop next-generation graduate and medical education. The successful candidate will serve as a catalyst for cross-disciplinary collaboration, advancing discovery, and improving health outcomes on a broad scale. The mission of the Schmidt College of Medicine as a community-based medical school is to advance the health and well-being of our community by training future generations of humanistic clinicians and scientists and translating discoveries to patient-centered care. We are committed to providing educational programs that enable our trainees to become active, self-directed, lifelong learners who understand the centrality of collaborative relationships in healthcare research and delivery. We are looking for someone with visionary thinking and a passion for ultimately advancing scientific knowledge and healthcare delivery. The successful candidate will lead and contribute to cutting-edge research, education, and strategic initiatives at the intersection of artificial intelligence, population health, public health, healthcare delivery, and health services research.

Requirements

Ph.D., M.D., or equivalent degree or experience in applying artificial intelligence, machine learning, or advanced analytics in healthcare, clinical, population health, public health, or biomedical research settings.
Demonstrated AI and/or machine learning expertise with relevance to biomedical, public health, or clinical challenges.
Strong research publication record and proven (for senior roles) or potential (for junior roles) success in obtaining external funding, publications, and successful mentoring of junior faculty, postdoctoral fellows, and/or graduate students.
Commitment to interdisciplinary collaboration and educational excellence.
Working knowledge of ethical, regulatory, privacy, and governance considerations related to the use of AI in healthcare and biomedical research, including data security, bias mitigation, and responsible AI practices.
Experience working with healthcare data sources, such as electronic health records (EHRs), claims data, registries, clinical research datasets, or population health databases.

Nice To Haves

Experience developing, implementing, evaluating, or advising on AI governance frameworks, policies, standards, or best practices within healthcare, public health, academic medical centers, government agencies, or healthcare organizations.
Experience applying AI and machine learning methodologies to public health, population health management, health equity, healthcare operations, quality improvement, or community health initiatives.
Demonstrated success translating AI innovations into real-world clinical, public health, or healthcare delivery environments.
Experience collaborating with healthcare systems, public health agencies, community organizations, or other external stakeholders on AI-driven initiatives.
Expertise in emerging areas of AI in healthcare, including generative AI, large language models, clinical decision support, predictive analytics, precision medicine, digital health, and learning health systems.
Experience evaluating the ethical, legal, regulatory, and societal implications of AI technologies in healthcare and public health settings.
Knowledge of federal and state policies, guidance, and evolving best practices related to the deployment of AI in healthcare and public health environments.
Documented success in cross-disciplinary collaboration and research between basic science and clinical faculty.
Engagement in curriculum development or AI educational programming in health sciences.

Responsibilities

Collaborate with researchers throughout the College and University to integrate AI into ongoing translational and clinical projects.
Contributes to basic and applied research activities and authors scientific publications, technical and agency reports, and patent preparations.
Contribute to institutional AI strategic efforts.
Develop, lead, and sustain a competitive, externally funded research program (e.g., NIH, NSF, DoD, industry) applying AI/ML methodologies across cutting edge research including observational, clinical, community-based, health services research projects and health outcomes research.
Disseminate findings in high-impact journals and at national and/or international conferences.
Design, implement, and evaluate AI-focused coursework for graduate, medical, and health professional education.
Mentor College of Medicine graduate students, medical students, residents, postdocs, and faculty in the development, utilization and policies of AI methodologies in healthcare, clinical, population health, public health, or biomedical research settings.
Embed AI modules into existing core and elective courses in the College of Medicine.
Serve as a resource for developing new educational programs in the College of Medicine.
Serve as an AI resource for faculty and researchers, offering expertise and guidance on data science integration across departments.
Partner with program directors to identify AI integration opportunities in existing and future graduate curricula.
Contribute to departmental, college, and university committees, and participate in strategic AI-related initiatives and help shape the College’s AI strategy.
Build and sustain partnerships with industry, government agencies, and academic collaborators to foster AI-driven research, training, and innovation.
Collaborate with clinical partners to develop learning health systems initiatives that narrow the gap between evidence and practice.