Description
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
- Integrative modeling of biological and physiologic systems
- Foundation models for biological data
- Causal AI for biological systems
- Translational research, industry partnerships, and clinical entrepreneurship
- Pediatric and maternal-fetal health applications
- Computational pharmacology, pharmacokinetic modeling, and drug discovery
- Robotics, medical devices, and hardware innovation for anesthesia and pain management
- Implementation science, operational AI, and healthcare delivery optimization
Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. Stanford welcomes applications from all who would bring additional dimensions to the University’s research, teaching and clinical missions.
Consistent with its obligations under the law, the University will provide reasonable accommodations to applicants and employees with disabilities. Applicants requiring a reasonable accommodation for any part of the application or hiring process should contact disability.access@stanford.edu.
The university’s central functions of research and education depend on freedom of thought, and expression. The Anesthesiology Department, School of Medicine, and Stanford University value faculty who will help foster an open and respectful academic environment for colleagues, students, and staff with a wide range of backgrounds, identities, and perspectives. 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 values
Application Instructions
Submit CV, cover letter, research statement, and teaching statement via Stanford Faculty Positions. The position will remain open until filled; review of applications begins June 1, 2026.
Questions may be directed to: Nima Aghaeepour, Professor of Anesthesiology, Perioperative and Pain Medicine, at naghaeep@stanford.edu.
This role is open to candidates from multiple disciplines/specialties. The pay offered to the selected candidate will be based on their field or discipline. The expected base pay range for likely disciplines are listed below. Interested candidates whose discipline is not listed below may contact the hiring department for the salary range specific to their discipline/specialty.
MD Anesthesiologist
- Assistant Professor: $447,000 – $457,000
- Associate Professor: $471,000 – $481,000
- Professor: $494,000 – $515,000
PhD Data Scientist
- Assistant Professor: $228,000 – $257,000
- Associate Professor: $274,000 – $301,000
- Professor: $325,000 – $398,000
This pay range reflects base pay, which is based on faculty rank and years in rank. It does not include all components of the School of Medicine’s faculty compensation program or pay from participation in departmental incentive compensation programs. For more information about compensation and our wide-range of benefits, including housing assistance, please contact the hiring department.
Stanford University has provided a pay range representing its good faith estimate of what the university reasonably expects to pay for the position upon hire. The pay offered to the selected candidate will be determined based on factors including (but not limited to) the experience and qualifications of the selected candidate including equivalent years in rank, training, and field or discipline; internal equity; and external market pay for comparable jobs.
