Create and release your Profile on Zintellect – Postdoctoral applicants must create an account and complete a profile in the on-line application system. Please note: your resume/CV may not exceed 3 pages.
Complete your application – Enter the rest of the information required for the IC Postdoc Program Research Opportunity. The application itself contains detailed instructions for each one of these components: availability, citizenship, transcripts, dissertation abstract, publication and presentation plan, and information about your Research Advisor co-applicant.
Additional information about the IC Postdoctoral Research Fellowship Program is available on the program website located at: https://orise.orau.gov/icpostdoc/index.html.
If you have questions, send an email to ICPostdoc@orau.org. Please include the reference code for this opportunity in your email.
Research Topic Description, including Problem Statement:
Modern imagery-based intelligence collectors produce an enormous amount of data, which is simply not possible for a human to review in a timely manner. Effective use of machine learning or artificial intelligence algorithms can meet the timeliness requirements of intelligence products but requires truth in the form of labeled data in order to train networks to detect, classify, and/or identify objects of interest. In the classified domain, labeled data is typically produced via either expensive and logistically complicated coordinated ground truth campaigns, or a tedious and error prone manual process. The challenge for the IC is to develop methodologies for unsupervised learning and labeling of large imagery databases that can be transferred from unclassified systems to classified ones.
The use of surrogate algorithms to establish relative truth sets with minimal human intervention is one potential approach. Novel concepts, such as techniques to transfer robustly trained models between domains while maintaining performance would also be considered.
Relevance to the Intelligence Community:
Key Words: Labeling, Machine Learning, Artificial Intelligence, Training, Computing, Data, Sensors, Space