Basic Life Research Scientist (1 Year Fixed Term)

Stanford University School of Medicine; Palo Alto (CA); On-site; USD 80,000 - 100,000; Full time

Job summary

Stanford University School of Medicine's Aghaeepour Lab seeks a Basic Life Research Scientist to advance AI/ML methods in clinical medicine, integrating multimodal biomedical data including EHRs and physiological signals.

The role involves leading research projects, mentoring trainees, contributing to manuscripts, and collaborating with clinicians and data scientists in a rigorous translational environment in Palo Alto.

Qualifications

• PhD in a quantitative field required.
• Experience applying ML/DL to real-world datasets.
• Strong programming skills in Python and ML frameworks.

Responsibilities

• Contribute to and/or lead research projects involving ML and clinical data integration.
• Develop, implement, and evaluate AI/ML models using large-scale biomedical datasets.
• Provide technical mentorship and guidance to trainees.
• Contribute to manuscript preparation, review, and scientific dissemination.
• Collaborate with clinicians, data scientists, and external partners.
• Help maintain rigor, reproducibility, and code quality.

Skills

ML

Programming

Interdisciplinary Collaboration

Education

PhD in Computer Science or related field

Tools

Python

PyTorch

TensorFlow

Job description

Job Description

The Department of Anesthesiology, Perioperative, and Pain Medicine, at Stanford University’s School of Medicine, is a world-leading department that offers comprehensive training and perioperative patient care, pain management, and critical care medicine as well as cutting-edge research, encompassing a wide spectrum of programs in basic, translational, clinical, health services and medical education.

The Aghaeepour Lab in the Department of Anesthesiology, Perioperative and Pain Medicine is seeking a Basic Life Research Scientist. The Basic Life Research Scientist will join an interdisciplinary AI/ML research lab focused on developing and deploying machine learning methods in clinical medicine. The successful candidate will contribute to research projects involving machine learning and multimodal biomedical data; provide technical input and mentorship to trainees; contribute to manuscript development; and collaborate with clinical and computational teams. The Aghaeepour Lab develops machine learning methods to integrate multimodal biomedical data—including electronic health records, physiological monitoring, and omics data—to improve outcomes in acute care settings such as the operating room, ICU, and NICU. At Stanford University School of Medicine, the work we do touches the lives of those today and tomorrow. Through education, research, and health care, the School of Medicine improves health through leadership, diversity, collaborative discoveries, and innovation in health care. Whether working in departments with faculty, or in units that support the school, our staff are a part of teams that propel us on our journey toward the future of medicine and Precision Health.

Stanford is rooted in a culture of excellence and values innovation, collaboration, and life-long learning. To foster the talents and aspirations of our staff, Stanford offers career development programs, competitive pay that reflects market trends, and benefits that increase financial stability and promote healthy, fulfilling lives. An award-winning employer, Stanford offers an exceptional setting for professionals looking to advance their careers.

The Department of Anesthesia are committed to diversity, equity, and inclusion for its faculty, staff, residents, postdocs, and fellows. We aim to recruit, support, retain, and promote diversity in our department.

For more information on our department, please see our website: https://med.stanford.edu/anesthesia.html

The expected pay range for this position is $80,000 to $100,000 per annum.

Stanford University has provided a pay range representing its good faith estimate of what the university reasonably expects to pay for the position. 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 since their applicable education, field or discipline; departmental budget availability; internal equity; among other factors.

At Stanford University, base pay represents only one aspect of the comprehensive rewards package. The Cardinal at Work website ( https://cardinalatwork.stanford.edu/benefits-rewards ) provides detailed information on Stanford’s extensive range of benefits and rewards offered to employees. Specifics about the rewards package for this position may be discussed during the hiring process.

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 mission.

Core Duties

• Contribute to and/or lead research projects involving machine learning and clinical data integration
• Develop, implement, and evaluate AI/ML models using large-scale biomedical datasets
• Provide technical mentorship and guidance to trainees
• Contribute to manuscript preparation, review, and scientific dissemination
• Collaborate with clinicians, data scientists, and external partners
• Help maintain high standards of rigor, reproducibility, and code quality
• other duties may also be assigned

Qualifications

Minimum Requirements

• Ph.D. in Computer Science, Biomedical Informatics, Statistics, Engineering, or a related quantitative field
• Experience in machine learning, deep learning, or AI applied to real-world datasets
• Strong programming skills (e.g., Python, PyTorch, TensorFlow)

Additional Desired Qualifications

• Experience working with biomedical or clinical data (e.g., EHRs, physiological signals, omics)
• Strong publication record or demonstrated research productivity
• Ability to work in interdisciplinary teams spanning clinical and computational domains
• Experience in areas such as time series modeling, multimodal learning, or causal inference