AI/ML Research Scientist - Biomedical & Clinical Data

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 Anesthesiology, Perioperative and Pain Medicine department is seeking a Basic Life Research Scientist to advance machine learning methods training with multimodal biomedical data in clinical settings. The role emphasizes collaboration with clinical and computational teams and manuscript contribution.

The position focuses on developing AI/ML approaches for healthcare data, including EHRs and physiological signals, supporting perioperative and critical care

Qualifications

• 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).

Responsibilities

• 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.

Skills

Machine learning

Deep learning

AI application

Education

Ph.D. in Computer Science, Biomedical Informatics, Statistics, Engineering, or related quantitative field

Tools

Python

PyTorch

TensorFlow

Job description

Stanford University School of Medicine’s Anesthesiology, Perioperative and Pain Medicine department is seeking a Basic Life Research Scientist to advance machine learning methods training with multimodal biomedical data in clinical settings. The role emphasizes collaboration with clinical and computational teams and manuscript contribution.

The position focuses on developing AI/ML approaches for healthcare data, including EHRs and physiological signals, supporting perioperative and critical care