Stanford Autonomous Agents Lab, in conjunction with The Stanford Center on Early Childhood, is seeking to hire a postdoctoral research associate in the area of impact-driven artificial intelligence in healthcare, early childhood learning, and education. Under the joint direction of Professors Nick Haber and Philip Fisher, the postdoc will join and lead portions of an ongoing project to develop AI-based tools that enable and automate large-scale data analysis and clinical/learning interventions. As a starting point, we aim to use computer vision and automated speech recognition techniques to partially or fully automate aspects of the FIND Program, with the goal of dramatically increasing access to this proven child/caregiver interaction coaching intervention. FIND is an evidence-based video coaching program that has been documented to improve responsive and supportive interactions for parents of young children and for childcare professionals/early childhood educators. More information about FIND is available here.
This role will involve:
- engineering of AI-based tools on image, video, and other data,
- leading a team of graduate students, undergraduate students, and staff, with a focus on day-to-day student mentorship,
- studying and refining the real-world deployment of AI-based tools, from the perspectives of human-computer interaction and educational and clinical impact,
- using these tools to analyze large-scale human behavioral data, and
- publication on a wide range of topics (including work on AI tool engineering, human-computer interaction, clinical and educational application efficacy, and human behavioral analyses).
The role supports both entrepreneurial (building AI-based tools for real-world deployment) and academic (supporting development of a lab in impact-focused AI) ambitions. While we anticipate the possible need to develop new AI tools, the role’s focus will be on effective application, not on the development of fundamental AI techniques.
The candidate must have a PhD and extensive experience in modern deep neural network-based techniques. The ideal candidate should be:
- well-versed in current techniques in computer vision and automated speech recognition, with experience in real-world technology deployment,
- passionate about child health and development,
- experienced in leading a research team.