The Community Engaged Texas Research Alliance (CENTRAL) and The Institute for Mental Health Research (IMHR) at the University of Texas, Austin seek an outstanding individual for a postdoctoral fellow position in mobile sensing and mental health. The ideal individual would have expertise in one or more of the following domains related to wearable sensors:
- signal processing
- feature generation
- machine learning
- digital phenotyping
- mobile health interventions including wearable sensors
The postdoctoral researcher will serve a unique role by facilitating interactions between multiple research areas (psychology, computer science and engineering) with the goal of identifying opportunities and barriers to develop and deploy cutting edge technologies in mobile sensing.
The post-doctoral fellow will be appointed in the IMHR (https://liberalarts.utexas.edu/imhr/) and would be supervised by IMHR faculty, including Drs. Christopher Beevers, David Schnyer, and Kaya deBarbaro.
The mission of the IMHR is to develop a novel, interdisciplinary, and high impact research program that incorporates scientific discoveries from neuroscience, computer science, and informatics into how psychosocial treatments are developed and delivered to patients. Initial focus of this postdoctoral fellowship will be on studying the use of smartphone mobile sensing among depressed adults in a clinical trial designed to improve negative thinking.
The goal of The Central Engaged Texas Research ALiance is to study the effects of early life adversity on health and development by building an innovative, replicable, field-based research and intervention model that leverages advances in community engagement, bio-behavioral and environmental measurements, and system data informatics.
Applicants should have a doctoral degree in computer science, computer engineering or a field related to data analytics of wearable sensors, and excellent skills in programming, communication, and writing. Minority applicants are encouraged. Appointment is for at least two years, contingent on first-year performance. Salary and benefits are competitive.