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 2 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:
Smart cities are physically distributed and typically uncontrolled environments within which a wide range of devices are deployed. To ensure the security of their environments, system integrators and maintainers of smart cities need to have comprehensive awareness of devices within their smart city networks, and confidence that those devices are not being impersonated. While some of this functionality may be provided by network protocols used in a smart city, the heterogeneity of current smart city networks does not present a clear mechanism for system-wide device identification and authentication. With respect to the maturation of machine learning techniques for smart city applications, this project would be expected to:
Suggested approaches would include:
Relevance to the Intelligence Community (IC):
Smart Cities and similar initiatives are large-scale deployments of interconnected systems and devices that observe, analyze and act upon data to provide a service or function to the public. The union of discrete technology stacks at a large scale is what provides the utility of a smart city. Technologies deployed at such a scale promise to improve the lives of citizens, efficiently deliver essential and ancillary services, and increase economic productivity with minimal user interaction. The highly connected nature of a smart city creates a unique risk profile that must be considered. Increased scale and complexity create novel risks and amplify those already known. Identifying and authenticating devices in this environment is a critical first step in helping risk owners appropriately manage and secure their systems. Information relevant to the intelligence community could be prioritized as follows:
Relevance to the Intelligence Community:
This topic addresses the Artificial Intelligence/Machine Learning priority, by examining data interoperability (protocol-agnostic connections in smart city networks) and multi-modal approaches to solving intelligence problems.
Key Words: #Complex Systems, #Architecture, #Machine Learning, #Device Discovery, #Smart Cities, #IoT, #IIoT, #Cyber Security, #Data Verification, #Authentication, #Identification