Assistant/Associate/Full Professor - AI in Health

Assistant/Associate/Full Professor - AI in Health . Arizona State University: Office of the University Provost . Multiple: Downtown Phoenix, Polytechnic, Tempe, West Valley . ASU Health is an innovative learning health ecosystem formed to accelerate and focus the university’s efforts to improve the lives, healthcare access, and health outcomes of Arizonans and communities beyond.

Grounded in a bold and creative approach, this enterprise targets urgent regional health needs through solutions focused on prevention, the tight coordination of public health and healthcare delivery, and the promotion of health for all communities. By integrating modern technology and 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, mana