Postdoctoral Scholar in AI and Digital Health Innovation

Position title: Postdoctoral Scholar

Salary range: The posted UC Salary Scales set the minimum pay determined by rank and step  Table 23:
Postdoctoral Scholar-Employee, Postdoctoral Scholar-Fellow, Postdoctoral Scholar-Paid Direct, Fiscal Year.

Open date: October 13, 2026

Next review date: Monday, Nov 2, 2026 at 11:59pm (Pacific Time)

Final date: Thursday, Dec 31, 2026 at 11:59pm (Pacific Time)

Do you want your AI research to move beyond benchmarks and make an impact in real-world healthcare? UCLA's Center for Advanced Surgical and Interventional Technology (CASIT), part of the Department of Surgery at the David Geffen School of Medicine, is seeking a highly motivated Postdoctoral Scholar with strong expertise in computer vision, multimodal AI, machine learning, and/or agentic AI to help build the next generation of intelligent digital health solutions.

You will work side by side with clinicians, engineers, researchers, and industry partners to transform multimodal health data-including wearable and sensor data, medical images and video, speech, human movement, physiological signals, and clinical information-into AI-powered tools for digital biomarkers, remote monitoring, precision health, clinical decision support, and intelligent surgical systems, with the ultimate goal of translating AI innovation into measurable improvements in patient care.

We are looking for a researcher with a Ph.D. in Computer Science, Biomedical Engineering, Electrical and Computer Engineering, Data Science, Robotics, Computational Health, or a closely related field and a strong record in AI, machine learning, digital health, or biomedical data science. You will lead interdisciplinary projects that apply advanced computational methods to clinically meaningful problems across the continuum of care—from early detection and functional assessment to intervention, surgery, recovery, and monitoring or clinical experience is not required;

scientific curiosity, strong quantitative skills, and a genuine interest in learning from clinicians and solving real-world healthcare problems are essential.

• Digital biomarkers and multimodal health assessment:
  Develop AI models that integrate wearable sensors, movement, speech, physiological signals, imaging, video, and clinical data to characterize health status, frailty, resilience, cognitive-motor function, and disease progression.
• Wearables and remote monitoring:
  Transform longitudinal data from wearable and connected technologies into clinically actionable measures for monitoring patients in the hospital, at home, and in the community.
• Computer vision for healthcare:
  Apply computer vision to medical images, wound images, surgical video, and other visual health data for automated assessment, monitoring, prediction, and decision support.
• Agentic and generative AI for clinical care:
  Develop systems based on large language and vision-language models that reason across multimodal data, interact with tools, support clinical workflows, automate documentation, and enable personalized monitoring and care coordination.
• AI for brain and functional health:
  Combine speech, mobility, dual-task performance, wearable sensing, and clinical biomarkers to better characterize brain health, intrinsic capacity, and resilience to age- and disease-related decline.
• Intelligent surgical systems:
  Develop AI approaches for surgical video understanding, workflow analysis, skill assessment, robotic surgery, human-robot collaboration, and perioperative decision support.
• Digital interventions and precision health:
  Use data from telehealth, exercise, rehabilitation, and connected health programs to identify treatment response, engagement, and…