MD and FL-Human Research & Engineering Dir-FFP, Human Research and Engineering Directorate - FFP
||Aberdeen Proving Ground, MD 210055425
Basic science research is needed to address critical knowledge gaps underlying efforts to seamlessly integrate humans and advanced technology for future military and civilian applications. Broad needs defined within this problem space demand high-quality, complex experimentation combined with advanced quantitative analysis and modeling of human brain-body states and their dynamics in simulated and actual (real-world) operational contexts. More specifically, the Real-World Neuroimaging program at the US Army Research Laboratory (ARL) has the goal of furthering our understanding of how laboratory-based research findings in human performance translate to real-world situations.
Real-world neuroimaging technologies at ARL represents developing and executing experimental research, which furthers our ability to assess brain activity as it occurs within real-world settings, where neuroimaging conditions are less than ideal and often target cognitive states that are elusive in traditional laboratory scenarios. Example topic areas include (1) development and testing of novel neuroimaging hardware, such as dry, non-metallic, highly flexible sensors for EEG; ultra-low-power system design; and custom-adaptive cap design; (2) development and use of novel methods and tools for assessing data quality, such as EEG “phantoms” and new analytical comparison techniques; (3) novel software algorithms for dealing with motion artifacts, improving data SNR, and improving interpretation of noisy data; and (4) methods for displaying and interpreting EEG data in real-time.
The primary location is at ARL at Aberdeen Proving Ground, MD, but could involve travel to partner universities. We seek a Research Associate to assist with all aspects of the process from designing and executing experiments, to publishing and presenting findings.
Applicants must have earned a doctoral degree (PhD) in Cognitive Science, Psychology, Neuroscience, Biomedical Engineering, Human Factors, Bioinformatics, or a closely related discipline. A strong preference is afforded to candidates capable of independent, end-to-end data analysis including acquisition, signal conditioning and data reduction, visualization, and statistical analysis and/or quantitative modeling. Substantial high-level programming experience (i.e., MATLAB, Python, C/C++, R), is heavily preferred. Candidates should demonstrate a strong scientific background with examples of recent high-quality first-author presentations and writing. Strong candidates will have already successfully published (or had work accepted for publication) in one or more peer-reviewed journals.
McDowell K, Lin CT, Oie KS, Jung TP, Gordon S, Whitaker KW, Li SY, Lu SW, Hairston WD: Real-world Neuroimaging Technologies. IEEE Access 1(1): 131-149, 2013
Ries AJ, Touryan J, Vettel J, McDowell K, Hairston WD: A comparison of electroencephalography signals acquired from conventional and mobile systems. Journal of Neuroscience and Neuroengineering 3 (1): 10-20, 2014
Oliveira AS, Schlink BS, Hairston WD, Konig P, Ferris DP: Proposing metrics for benchmarking novel EEG technologies towards real-world measurements. Frontiers in Human Neuroscience 10: 2016
Neuroscience; Bioinformatics; Technology; Neuroimaging; Biomedical engineering; EEG; Real-world; Sensors; Behavioral Psychology;