Data Science Faculty Position

The Stanford Department of Anesthesiology, Perioperative and Pain Medicine in the School of Medicine invites applications for an open-rank faculty position focused on artificial intelligence, machine learning, data science, neural engineering, computational neuroscience, and physiological data science relevant to anesthesia, perioperative medicine, pain, and critical care. The appointment will be as Assistant Professor, Associate Professor, or Professor in the University Medical Line, University Tenure Line, or Non-Tenure Line (Research). A PhD or MD (or equivalent degree) is required.

• 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 rank and line will be determined by the qualifications and experience of the successful candidate. We are particularly interested in candidates developing AI-enabled methods for neural and physiologic signals, such as EEG-based brain-state modeling, computational neurophysiology of anesthesia and consciousness, neural signal decoding, computational pain neuroscience, neuromodulation, and perioperative or critical care monitoring, as well as integrative approaches that connect molecular, cellular, physiologic, and clinical data, as well as AI methods applied to basic science and biological datasets.

The successful candidate will join a highly collaborative environment developing computational approaches to understand brain, physiologic, and biological systems across scales. The department has strong expertise in clinical AI, perioperative data science, translational machine learning, and basic science research, and seeks to expand its strengths in emerging technological and clinical areas. The department provides a unique environment for research connecting engineering, neuroscience, physiology, biology, and clinical medicine, with access to operating rooms, ICUs, NICUs, perioperative monitoring systems, large-scale physiologic datasets, and Stanford’s extensive infrastructure for biological and multi-omics research.

Stanford also provides extensive infrastructure for biological and multi-omics research, including collaborations with basic science departments, the Wu Tsai Neurosciences Institute, the Stanford Institute for Immunity, Transplantation and Infection (ITI), the Maternal & Child Health Research Institute (MCHRI), and Stanford Bio-X.

Responsibilities

• Establish and maintain an independent research program in AI-enabled neurophysiology, neural engineering, physiologic data science, or AI-enabled biological discovery.
• Develop computational and/or engineering approaches for analyzing neural and physiologic signals and high-dimensional biological and experimental datasets.
• Build collaborative research programs across Stanford Medicine, Bioengineering, Neuroscience, basic science departments, and partner institutions.
• Contribute to teaching and mentoring of graduate students, postdoctoral fellows, residents, and clinical trainees.
• Provide clinical care in perioperative, pain, or critical care settings (for clinician candidates).

Potential Areas of Research Interest (Including but Not Limited To)

• Artificial intelligence and machine learning for medicine
• Anesthesia neuroscience and brain-state modeling
• Computational neuroscience and neural dynamics
• Neural engineering and neuromodulation
• Physiologic signal processing and monitoring
• Computational pain science
• Perioperative physiology and recovery
• Critical care physiology
• AI methods for neural and physiologic data
• Perioperative medicine and surgical recovery
• Brain health, neuroinflammation, and neurological disease
• Cardiovascular and critical care medicine
• Pain medicine and recovery trajectories
• Maternal and child health
• Immunology and inflammation
• Precision nutrition and metabolism
• Human-AI collaboration in medicine
• Artificial intelligence for multi-omics and systems biology
• Computational immunology
• Machine learning for biological and molecular datasets
• AI for cellular and biological imaging-based phenotyping
• AI for clinical imaging modalities and diagnostic imaging