The NeuroCognitive Translation lab at the University of Nebraska-Lincoln (PI: N.A. Hubbard, Ph.D. (https://psychology.unl.edu/nct-lab) invites applications for a full-time Postdoctoral Researcher in Translational Neuroscience. We are looking for a postdoctoral researcher to oversee data collection (~20% time), analyze brain imaging and behavior data (~30% time), and author manuscripts and grants (~50% time) based on ongoing projects pertaining to the prediction of psychiatric and behavioral health outcomes. Funding is available for up to two years, with availability for a third year dependent upon progress.
The NeuroCognitive Translation Lab applies expertise from behavioral, brain imaging (e.g., fMRI, DTI, fNIRS), and computational (e.g., machine learning, network analysis, pattern analyses) techniques to answer two research questions: (1) What can our neural and psychological signatures tell us about our mental abilities, and (2) How can we better measure and interpret these signatures for predicting human health outcomes?
The lab is housed in the Center for Brain, Biology and Behavior at the University of Nebraska-Lincoln (http://cb3.unl.edu/), which has a state-of-the-art Brain Imaging Center and a 3T MRI scanner dedicated for research. Beyond having access to the scanner, the postdoctoral researcher will also have access to EEG/ERP, virtual reality, mobile psychophysiology, eye-tracking (many of which can be measured both in and out of the MRI scanner), as well as several other cutting-edge techniques
1. Ph.D. and relevant research experience in cognitive neuroscience or related fields (e.g., computer science, engineering and physics). 2. Excellent written skills, including evidence of successful manuscript writing (first-author publications) and publication productivity. 3. Excellent organizational, interpersonal, and oral communication skills.
1. Expertise in neuroimaging software (e.g., FSL, AFNI, FreeSurfer). 2. Strong mathematical and programming skills using Linux, Matlab, R, Python, etc. A strong background in machine-learning is highly desirable.