We are seeking a highly motivated postdoctoral fellow to be part of an interdisciplinary research alliance working to develop data analysis and management methods and tools for mobile brain/body imaging data in support of a research program in neuroergonomics (the study of the brain and body at work). The research alliance seeks to discover relationships between brain dynamics (recorded by non-invasive EEG) and motivated behavior (recorded by body motion capture, eye tracking and other sensors) in interactive, information-rich human-system operating environments with an overall goal of developing performance enhancement and monitoring technology.
The ideal candidate will have a strong background in computation, machine learning, data management and/or visualization and have an interest in applying computational tools to large-scale problems in neuroscience.
The fellow will be based near Baltimore, Maryland at the Army Research Laboratory (ARL), Aberdeen, MD, where they will collaborate with a group of Army-funded government and industry researchers in gathering and analyzing data from successively more complex and realistic experiments. The successful applicant will be hired by and will work closely with the CANCTA research group at the University of Texas at San Antonio led by Kay Robbins of Computer Science and Yufei Huang of Electrical and Computer Engineering. The fellow will also interact with partner groups at UC San Diego, University of Michigan, University of Osnabrück, and National Chiao Tung University. In addition to participating in this unique large-scale analysis project, the fellow will be present the research at conferences and in the open research literature.
Salaries will be competitive. Transitions to permanent government or industry research positions may be available for successful candidates.
Minimum Requirements: Ph.D. with research experience in computational approaches to data analysis. The candidate must be an American citizen.
Preferred Qualifications: Strong computational skills with experience in machine learning applied to data from complex experimental designs.
For additional information please contact: Department of Computer Science University of Texas at San Antonio