To apply, please email the required application materials to Prof. Pablo Paredes (“pparedes [at] stanford [dot] edu”)
Join the Pervasive Wellbeing Technology Lab, led by Prof. Pablo Paredes, to create the next revolution of evidence-based wellbeing and precision mental health field technology. We focus on understanding the role technology can play to help people stay healthy and strong. We emphasize prevention and sustained health as opposed to simply predicting disease or infirmity.
In the lab, we leverage human-computer interaction (HCI), human-centered AI (HAI), human-centered design, and data analysis methods to advance the science of stress management "in the wild" by focusing on large-scale projects that integrate research on unobtrusive, passive sensing and algorithms that can help deliver suites of interventions embedded in commonly used tools (e.g., web browsers, mobile devices, computers, messaging apps, social media).
Join us if you are interested in taking research on mental health and wellbeing technology to the next level by combining human-centered and AI evaluation approaches to design precision health tools that can embrace ambiguity, complex/changing personal preferences, and operations in real environments.
Postdoctoral Scholars are hired on a full-time basis and compensated accordingly following the policies and pay scales set by Stanford University. We are open to considering people not relocating, i.e. working from home or from their hometown (in the US or around the world).
To learn more about our research program, visit: http://med.stanford.edu/pervasivewellbeingtech.html
We are looking for a postdoctoral scholar with a background in computer science, information science, computational social science, computational statistics, or similar to join the lab this Fall 2020. Candidates should also have some experience with the following:
- Human-centered design or HCI
- Applied AI, Machine Learning, NLP, and/or Deep Learning
- Designing, building and/or managing front-facing systems with multiple devices and multiple interaction points
- Obtaining, managing, and analyzing large-scale noisy/incomplete/multi-factorial data, especially using remote systems..
- Utilizing Amazon Mechanical Turk or similar for data collection and processing
- Intervention design and evaluation (i.e., efficacy, adherence, and engagement)
- A brief statement of interest
- Your CV
- Any recommendation letters