Assistant/Associate/Full Professor - AI in Health

data-driven decision-making across its distinct schools/colleges and clinical affiliations, ASU Health creates a revolutionary space where the innovative application of artificial intelligence, machine learning, and digital surveillance platforms can directly address the structural, social, and cultural factors that impact well-being. Ultimately, this ecosystem leverages the university’s multifaceted engineering and computational strengths to develop a technologically sophisticated health workforce, translating use-inspired research into immediate, scalable public impact.

Guided by the ASU Charter, ASU Health seeks to be the national vanguard of AI for Scalable Health Impact. Rather than treating AI as an isolated technical specialty, this enterprise-wide initiative invites applications for multiple tenure-eligible or tenured positions at the rank of Assistant, Associate or Full Professor. This position may be an academic (9-month) or fiscal (12-month) appointment depending on the norms and requirements of the college in which a candidate is hired. Appointments are anticipated beginning August 2027 and will be located on one of ASU’s Phoenix Metro campuses. We are seeking computational, translational, clinical, and public health scientists who bring deep methodological expertise in advanced computing alongside a strong commitment to use-inspired research and innovation. These faculty will develop the advanced AI approaches, predictive models, clinical and educational tools, and digital interventions necessary to improve individual patient care, optimize frontline clinical workflows, and strengthen regional health infrastructure. While grounded in robust, foundational AI methodologies, the successful candidate will primarily have a proven track record in real-world implementation and deployment of health AI solutions. We are seeking innovative methodologists who see clinical workflows, public health infrastructure, and health system operations at scale as the primary environments they aim to transform.

Candidates will be evaluated based on their capacity to fill critical translation gaps across the health ecosystem, rather than along narrow departmental definitions. Recruitment will focus on four cross-cutting tracks:

Track A: Clinical & Frontline Translation

• Focus: Bridging the gap between raw biometrics, electronic health records, and individual clinical action, with a distinct emphasis on optimizing how edge-computed AI interfaces with human clinical care teams.
• Research Domains: Wearables for remote monitoring, AI-driven diagnostics and therapeutics (including generative AI models), multimodal data fusion (EHR + multi-omics), personalized health, Software as a Medical Device (SaMD), ambient generative AI for clinical documentation, wearable device data feedback loops, and cognitive ergonomics for seamless point-of-care clinical workflows.

Track B: Systems & Public Health Translation

• Focus: Leveraging large-scale health data systems, predictive computing, and behavioral informatics to track, model, and protect entire communities.
• Research Domains: Digital and applied epidemiology, disease outbreak prediction, automated modeling of social determinants of health (SDOH), mitigation of algorithmic bias in public datasets, automated identification of health misinformation, and scalable digital interventions designed to drive population-level behavior change and preventive care.

Track C: The Future of Health Systems

• Focus: Architecting the operational, economic, ethical, and structural frameworks for next-generation healthcare delivery, and building the foundational engineering required to operationalize AI safely at scale.
• Research Domains: AI-driven healthcare economics, overcoming clinical and research bottlenecks via intelligent translational automation, health data privacy architectures (such as federated learning across distributed networks), FHIR/HL7 data pipeline modernization, pre-clinical model development, and the creation of digital twin environments for macro-health system simulation.

Track D: Clinical AI Evidence & Oversight

• Focus: Defining the evidence standards, regulatory frameworks, evaluation methods, and oversight structures that govern health AI across clinical, public health, and population health settings from first human use and regulatory clearance through continuous post-market surveillance.
• Research Domains: Clinician-in-the-loop study designs that evaluate the clinician–AI dyad rather than the model alone, automation bias and selective adherence, the empirical limits of human oversight as a safety control, supervision capacity and deskilling over time, prospective evaluation of adaptive and continually learning systems, evidentiary thresholds for first human use (the IND/IDE analogue for AI), multi-site transportability, drift detection and re-qualification, clinical safety cases for systems that act rather than advise, and lifecycle governance from predetermined change control through de-adoption.

Essential functions

• Transdisciplinary, Extramural Research: Develop and maintain an internationally recognized, aggressively funded research portfolio. Lead major multi-investigator, center-level proposals targeting federal and philanthropic agencies (e.g., NIH, PCORI, NSF, DoD, DARPA, ARPA-H). Publish extensively in top-tier computational and health journals/conferences and develop open-source knowledge products, software, and predictive tools of public value.
• Next-Generation Curricular Design and Delivery: Co-architect and deliver a modernized, unified Health AI and Data Science curriculum spanning undergraduate and graduate degree programs.
• Workforce Retraining: Design modular, stackable micro-credentials and certificate pathways (e.g., Generative AI for Health Professionals, Public Health Data Modernization) to enable rapid, large-scale upskilling of the regional and national health workforce.
• Ecosystem Engagement: Actively embed research into ASU’s immediate clinical and civic testbeds – including the Health Observatory, Decision Theater, HonorHealth, ASU Health Clinics, and Mayo Clinic.
• Institutional Synergy: Establish explicit, active joint research collaborations and multi-PI grant proposals that contribute to ASU’s clinical and translational research enterprise at the intersection of AI and health within the first 18 months of appointment.

About the academic units

Recruited faculty will hold appointments in the tenure home most aligned with their expertise, with opportunities for joint appointments as appropriate for the candidate(s). Possible tenure homes include, but are not limited to:

• School of Technology for Public Health: Focuses on population-scale AI interventions, data engineering, digital epidemiology, environmental health, health behaviors, policy and implementation, surveillance, and civic/public health digital infrastructure.
• College of Health Solutions: Advances the use of AI and data science to improve health and health care, with strengths in precision health, clinical and multimodal data integration, biomedical informatics and diagnostics, workflow optimization, point-of-care decision support, implementation science, and the development and evaluation of AI-enabled approaches to improve individual and population health outcomes.
• Edson College of Nursing and Health Innovation: Focuses on transforming healthcare delivery through AI driven innovations including real-time patient monitoring, clinical decision support systems at the bedside, and human-AI collaboration in care teams. The college is pioneering AI tools in nursing education and expanding outreach to support remote learning options.
• John Shufeldt School of Medicine and Medical Engineering: Focuses on AI-driven clinical diagnostics and therapeutics (software as a medical device), translational biomedical sciences, advanced multi-modal data fusion (across medical imaging, electronic health record, -omics, and other data sources), AI integrated into medical devices (such as brain computer interface), and precision health approaches (AI informed clinical decision support tools).
• Ira A. Fulton Schools of Engineering: Focuses on the foundational algorithmic, computational, and systems engineering pillars of AI. Research domains include advanced machine learning, deep learning, computer vision, natural language processing, scalable cloud and edge-computing architectures, privacy-preserving data infrastructures, and the hardware-software codesign necessary to deploy robust, secure, and high-performance computing systems across healthcare delivery and public health environments.
• School of Molecular Sciences in The College of Liberal Arts and Sciences: Focuses on AI-enabled molecular discovery, with strengths in structural biology, protein structure and function, biophysics, biochemistry, molecular modeling, and the analysis of complex molecular interactions. Applications include elucidating mechanisms of disease, identifying biomarkers and therapeutic targets, accelerating drug discovery, predicting treatment response, and developing molecular diagnostics and biosensors for precision health.
• School of Life Sciences in The College of Liberal Arts and Sciences: Focuses on applying AI, machine learning, and computational approaches across the life sciences, with strengths in genomics and other multi-omics, bioinformatics, molecular and cellular biology, microbiology, neuroscience, systems biology, and evolutionary medicine. Applications include integrating complex biological data, identifying predictors of health and disease, advancing precision medicine and public health, and addressing the ethical and societal implications of AI-enabled biological research.
• Department of Psychology in The College of Liberal Arts and Sciences: Focuses on the development and application of AI, computational modeling, and advanced quantitative methods to understand cognition, behavior, development, neuroscience, and mental health. Strengths include precision mental health, digital phenotyping and assessment, prediction and prevention of behavioral health conditions, adaptive and technology-enabled interventions, human-AI interaction, and the responsible design and evaluation of AI tools that support psychological well-being across the lifespan and diverse populations.
• Sandra Day O’Connor College of Law: Focuses on the legal, regulatory, ethical, and governance challenges associated with AI in health and health care, including health data privacy and security, algorithmic accountability, discrimination and equity, liability, intellectual property, informed consent, and the regulation of AI-enabled diagnostics, therapeutics, and medical devices. The college also examines how legal and policy frameworks can protect patients, promote responsible innovation, and support the safe and equitable adoption of emerging health technologies.
• W. P. Carey School of Business: Focuses on the organizational and economic dimensions of AI-enabled health transformation, including health care operations, supply chain management, information systems, business analytics, entrepreneurship, workforce strategy, and technology adoption. Strengths include evaluating the value and performance of AI investments, developing sustainable business and delivery models, managing organizational change, and translating AI innovations into scalable improvements in health care access, quality, efficiency, and consumer experience.
• Watts College of Public Service and Community Solutions: Focuses on the development, implementation, and evaluation of AI-enabled solutions for public and social services, with strengths in behavioral health, social work, health equity, social determinants of health, community safety, public policy and administration, and community-engaged research. Emphasizes the responsible use of AI to improve service delivery, strengthen public-sector and nonprofit systems, support vulnerable populations, and advance equitable community health outcomes.

Recruited faculty will also leverage ASU’s Knowledge Enterprise (KE) which provides researchers with a comprehensive technology ecosystem that combines advanced computational resources, secure research environments and dedicated technical expertise. Through the Research Technology Office (RTO) and Research Computing, faculty have access to the infrastructure, datasets, services and collaborative support needed to accelerate discovery, develop AI-enabled solutions and address complex challenges across disciplines.

Research Technology & Faculty Support

• Scientific software engineering and research application support
• Research data management and data lifecycle planning
• Cybersecurity, cloud and cyberinfrastructure expertise
• Proposal development support for computationally intensive and data-driven research
• Research technology consulting and technology strategy development

Advanced Computing & Artificial Intelligence Capabilities

• Access to the Sol supercomputer and advanced high-performance computing resources
• The ASU AI Research Acceleration (AIR) Platform, expanding access to AI models and computational capacity
• Resources and expertise for artificial intelligence, machine learning and large-scale data analytics
• Support for developing, testing and scaling AI-enabled research applications
• Opportunities to collaborate with ASU’s interdisciplinary community of AI researchers

Secure & Regulated Research

• KE Secure Cloud environments for research involving sensitive, restricted or regulated data
• Secure computing, storage and cloud resources that support the full research data lifecycle
• HIPAA-aligned and compliance-focused environments for health and other regulated research
• Secure AI model development and analysis capabilities
• Support for multi-institutional collaborations and externally governed datasets via the Health Observatory

About Arizona State University

ASU is a comprehensive public research university, measured not by whom it excludes, but by whom it includes and how they succeed; advancing research and discovery of public value; and assuming fundamental responsibility for the economic, social, cultural, and overall health of the communities it serves.

Learn more about the ASU Charter and Arizona State University.

Qualifications

Required Qualifications:

• Ph.D., M.D., M.D./Ph.D., or equivalent terminal degree in Biomedical/Clinical Informatics, Computer Science, Data Science, Biostatistics, Systems Engineering, Nursing Informatics, Health Services Research, Public Health, or a closely related health field.
• A demonstrated record of excellence in research, peer-reviewed publication, and technology translation consistent with rank.
• A demonstrated commitment to the ASU Charter, with an emphasis on advancing use-inspired research, scaling technology for the public good, and ensuring inclusive excellence within the digital health landscape.

Desired Qualifications for the Rank of Assistant Professor:

• Demonstrated track record or strong potential for securing extramural research funding (e.g., NIH, NSF, ARPA-H, PCORI) as a primary PI or Co-I.
• A high-quality publication record appropriate to rank in top-tier peer-reviewed journals and/or leading computational conference proceedings in health AI, biomedical informatics, or public health technologies.
• A defined, use-inspired research agenda that integrates advanced computing/AI with health, aligning with one or more of the recruitment tracks (Clinical & Frontline Translation, Systems & Public Health Translation, Future of Health Systems, or Clinical AI Evidence & Oversight).
• Experience in or demonstrated readiness for designing, scaling, or deploying AI tools and data platforms within real-world clinical, health system, laboratory, or public health settings.
• Evidence of navigating complex data governance, human factors, and regulatory frameworks (e.g., IRBs, HIPAA, compliance architectures) necessary for digital health translation.
• Evidence of successful teaching and curriculum development in public health, computer science, biomedical informatics, or related health AI domains at the undergraduate or graduate level.
• Experience mentoring undergraduate and/or graduate students in research projects, publishing, and career development.
• Active involvement or potential for leadership in institutional, community, and/or professional organization service.

Desired Qualifications for the Rank of Associate Professor:

• An established, sustained program of research supported by a continuous history of extramural funding as a primary PI (e.g., NIH, NSF, ARPA-H, PCORI, DoD, DARPA) sufficient to support graduate students and post-doctoral scholars.
• An extensive portfolio of high-impact, peer-reviewed publications and open-source knowledge products, software, or predictive tools in health AI and computational health disciplines.
• A national reputation for scholarly productivity and research impact in one or more of the cluster hire's core translation tracks.
• Proven track record of operationalizing and evaluating AI applications in clinical workflows, health operations, or public health digital infrastructure.
• Demonstrated capability in building transdisciplinary research bridges, leading multi-PI grant proposals, and establishing collaborative partnerships across computing, engineering, and health sciences.
• Sustained excellence in course design, instruction, and curricular innovation at the undergraduate and graduate levels.
• Demonstrated success in mentoring graduate students, post-doctoral fellows, and junior colleagues through successful research outcomes, publications, and degree completion.
• Active leadership and service within university committees, national/international professional societies, and peer-review bodies commensurate with the rank of Associate Professor.

Desired Qualifications for the Rank of Full Professor:

• An internationally recognized, aggressively funded research program demonstrated by a continuous, long-term track record of major extramural funding as primary PI.
• Demonstrated experience leading major multi-investigator or center-level research grants, large-scale cross-sector clinical/industry partnerships, or national computational health initiatives.
• A sustained, prestigious publication and dissemination record that has significantly advanced the field of AI in health, public health, or medicine.
• Demonstrated national and international leadership and visibility within health AI, biomedical informatics, digital epidemiology, or healthcare system transformation.
• Proven leadership in designing and scaling modular, innovative educational programs, micro-credentials, or workforce upskilling efforts in health AI and data science.
• A distinguished history of successfully mentoring junior faculty, post-doctoral scholars, and doctoral candidates toward independent, productive careers.
• Demonstrated success in supporting student retention, graduation, and inclusive excellence in computational and health disciplines.
• Exemplary record of high-level administrative, institutional, national, and international service and leadership commensurate with the rank of Full Professor.

Application Instructions

Applicants must submit the following materials electronically through Interfolio:

• Cover letter describing the applicant's interest in and qualifications for the position, including how their research aligns with the proposed tracks articulated above.
• Current curriculum vitae.
• Names and complete contact information for three professional references

Only electronic applications submitted through the designated Interfolio posting will be accepted.

Application deadline

Application deadline is November 30, 2026. Applications will continue to be accepted on a rolling basis for a reserve pool. Applications in the reserve pool may then be reviewed in the order in which they were received until the position is filled.

Questions about the position should be directed to Colleen Cordes, Assistant Vice Provost for Career-Track Faculty and Clinical Professor, colleen.clemency@asu.edu

Interfolio application process

Arizona State University uses Interfolio's Faculty Search to conduct academic personnel searches. Applicants receive a free Dossier account and may submit application materials, including confidential letters of recommendation, through the system.

Application Process

This institution is using Interfolio's Faculty Search to conduct this search. Applicants to this position receive a free Dossier account and can send all application materials, including confidential letters of recommendation, free of charge.

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