Friday, December 2, 2022

Summer 2023 Paid Internships: The Applied Research Laboratory for Intelligence and Security (ARLIS) @ UMD



The Research for Intelligence & Security Challenges (RISC) Initiative
Summer Internship for Difficult Security Problems
May 30 – August 4, 2023
Virtual & in College Park, Maryland

The deadline for applications is Monday, February 6th, 2023.

Learn more after the Information Session: Dec. 15, 2-3 pm
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The Applied Research Laboratory for Intelligence and Security (ARLIS) at the University of Maryland, College Park, is seeking outstanding undergraduate and graduate students to participate in the virtual Research for Intelligence & Security Challenges (RISC) Initiative internship program. This exciting 10-week paid program pairs students with faculty mentors from INSURE consortium member institutions and the Department of Defense (DOD) and Intelligence Community (IC) community, and offers the opportunity to be sponsored for a security clearance and to be considered for future employment with the U.S. government.


The RISC Initiative is particularly seeking interns with expertise in one or more of the following disciplines:
Mathematics and Statistics: Data analytics, quantitative modeling, experimental design, graph analytics;

Social & Behavioral Sciences: cognitive/neuroscience & psychology, sociology, criminal justice, teamwork and group dynamics, communications, disinformation and misinformation, social network analysis, anthropology, human geography (e.g., pattern of life/mobility modeling), political science, international relations;

Languages and Linguistics: languages of interest to global security including but not limited to Mandarin, Russian, Farsi, Korean, and Arabic; computational linguistics and natural language processing; natural language understanding;

Data Science: Data and knowledge engineering, data curation, tagging, metadata, repositories, data visualization, library sciences;

Additional topics may include: Measurement and evaluation of learning outcomes, environmental modeling and remote sensing, human factors, regulatory public policy.