Based on the foundation of the Ted and Karyn Hume Center for National Security and Technology, Virginia Tech launched the Virginia Tech National Security Institute in September 2021. With a presence in Blacksburg and the Washington, D.C., metro area, the institute aspires to be the nation's preeminent academic organization at the nexus of interdisciplinary research, technology, policy, and talent development, the national security institute will advance national security in pursuit of a secure America.

Motivated applicants are sought to join VT NSI as Research Assistant Professor, Research Associate Professor, or Research Professor in Statistics or Data Science to support rapid growth in our body of programs in data strategies, test and evaluation, artificial intelligence assurance, digital engineering, and cyber analytics and operations. Rank will be determined based upon experience and credentials. The winning candidates can expect highly competitive salary and benefits in a university setting. This position serves as a research staff member of the Hume Center and in the Intelligent Systems Division (ISD). ISD hosts a range of dynamic research projects and the successful applicant will have the ability to contribute to these programs, lead the development of new programs, and mentor students engaged in these programs.

Specific responsibilities include the following: (1) support the execution of university sponsored programs by conducting research and development; (2) author technical documents and publish peer-reviewed academic papers; (3) support the development of new funded research programs by participating on proposal teams and supporting the business development activities of VT NSI; (4) mentor students engaged in experiential learning programs; (5) advise graduate students pursuing advanced degrees; and (6) occasionally teach courses (optional).

We are seeking individuals with expertise in applied statistics and data science to include at a minimum some of the following: experimental design, survey sampling, control charts, and Bayesian models (hierarchical and Gaussian Process). Successful applicants will have experience applying these statistical methods to complex organization and/or system technologies. Experience using machine learning and artificial intelligence (for example neural networks and Markov decision processes) in detection, classification, forecasting, and control is desired, but not required.