Open Line, Open Rank, Artificial Intelligence Faculty Cluster Hire Search

Open Line, Open Rank, Faculty Cluster Hire Search
Stanford University School of Medicine

The Stanford University School of Medicine (SoM) is recruiting multiple faculty at the Assistant, Associate, or Full Professor in the University Tenure Line (UTL), University Medical Line (UML), or Non-Tenure Line-Research (NTL-R) through this AI (Artificial Intelligence) Faculty Cluster Hire Search. We are specifically interested in candidates who have experience developing and applying novel biomedical AI and data science methods that incorporate biomedical domain expertise to ensure relevance and impact to health and medicine. Candidates will be hired into one or more SoM department(s) and contribute to the research, educational, and if relevant, clinical activities.

This AI Faculty Cluster Hire Search aims to recruit a diverse group of experts dedicated to fostering growth of biomedical AI and data science both within our organization and beyond. These distinguished individuals will become integral members of a dynamic community, collaborating not only within their respective departments or institutes but also across the SoM and our university at large.

• The predominant criterion for appointment in the University Tenure Line is a major commitment to research and teaching.
• The major criteria for appointment for faculty in the University Medical Line shall be excellence in the overall mix of clinical care, clinical teaching, scholarly activity that advances clinical medicine, and institutional service appropriate to the programmatic need the individual is expected to fulfill.
• The major criterion for appointment for faculty in the Non-tenure Line (Research) is evidence of high-level performance as a researcher for whose special knowledge a programmatic need exists.

Faculty line and rank will be determined by qualifications and experience. The successful candidate must have an MD, MD/PhD, or PhD with substantial expertise in one or more aspects of biomedical data science enabled or enhanced by AI. The successful candidate will be expected to develop an independent research program that advances AI approaches to biomedical data science, with a focus on their use in basic, translational, clinical, and/or population sciences.

Examples of focus areas in basic science research include development of methods to determine molecular structures, accelerate development of novel therapeutics, elucidate stem cell biology, or enable regenerative medicine. Examples of focus areas in clinical research include the development of AI methods for integration and analysis of multimodal patient data, including laboratory tests, clinical notes, images and video across multiple scales, speech to text, physiologic assays, and functional evaluations. Clinical AI research domains span across medical specialties, including but not limited to cancer, neurology, neuroscience, cardiovascular disease, intensive care, mental health, peri-operative care, pain management, ophthalmology, pediatrics, radiology, pathology, and surgery. Examples of focus areas in population health research include pharmacoepidemiology, genetic epidemiology, environmental epidemiology, AI health policy, fairness, and the legal, regulatory, ethical, and economic considerations that underlie the responsible implementation of clinical decision support tools. Research in all of these areas will benefit from broad interactions and collaborations throughout the SoM, across Stanford University, and within the large and growing health systems of Stanford Medicine.

The successful candidate will be expected to teach students, residents, postdoctoral fellows and clinical fellows, and participate in relevant clinical and basic science conferences. They will have demonstrated the potential to achieve, or have a demonstrated record of achievement in relevant rigorous research. The Departments, School of Medicine, and Stanford University value faculty who will help foster an inclusive academic environment for colleagues, students, and staff with a wide range of backgrounds, identities, and outlooks. Candidates may choose to include as part of their research and teaching statements a brief discussion about how their work and experience will further these ideals. Additional information about Stanford's IDEAL initiative may be found here: https://ideal.stanford.edu/about-ideal.

Review of complete applications will begin on September 23, 2024, and will continue until the positions are filled.

Interested candidates should submit the following to apply:

1. A detailed letter of research and teaching interest and if relevant, clinical specialty,
2. A curriculum vitae,
3. Three names of referees for letters of recommendation.

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