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Opportunity at Davies Teaching Fellowships (ARL/USMA)

Understanding and Computational Modeling of Behavioral and EEG Signals during Visual Search


MD and FL-Human Research & Engineering Dir-FFP, Human Research and Engineering Directorate - FFP

RO# Location
AA.27.01.B8461 Aberdeen Proving Ground, MD 210055425


Name E-mail Phone
Hung, Chou Po 301.467.1144


To develop advanced autonomous systems, robots, and analytical tools, advances in machine learning must be coupled with improved understanding and modeling of human cognitive mechanisms and human behavior. ARL is looking for a Research Associate to further our goal of developing quantitative computational models of human behavior and neurophysiological (electroencephalography, EEG) signals during visual search. Research opportunities include experimental study of naturalistic visual search and other behavior, EEG analysis, and development of “deep” machine learning algorithms, computational models, and theory. Focus will be placed on advancing our fundamental understanding and developing predictive models of how scene dynamics affect EEG signals and visual search behavior. Ideally, candidates should demonstrate expertise in cognitive neuroscience (or a related discipline), EEG, visual search, human behavior, and/or computational modeling. All candidates must possess strong quantitative skills (e.g., Matlab, Python) and/or experience with EEG analysis software (e.g. EEGlab).



Chen YP, et al: Network anisotropy trumps noise for efficient object coding in macaque inferior temporal cortex. Journal of Neuroscience 35(27): 9889-9899, 2015.

Ries AJ, et al: The impact of task demands on fixation-related brain potentials during guided search. PLoS One 11(6): 2016, e0157260. doi: 10.1371/journal.pone.0157260


Vision; EEG; Machine learning; Visual search;


Citizenship:  Open to U.S. citizens
Level:  Open to Postdoctoral applicants
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