Science and Engineering Apprenticeship

Apprentice poster award winners and Honorable Mentions

Become a 2019 Apprentice

  • - Must be a graduating high school senior who will attend a senior college in Fall 2019
  • - Must be available to work June 6 through August 15, 2019 (Unpaid time off is allowed for attendance of freshman college orientation, otherwise there are no exceptions to the policy.)
  • - Must have both applied and been admitted to The University of Texas at Austin
  • - Must be a U.S. Citizen

About the Program

The Science and Engineering Apprenticeship is a competitive program for graduating high school seniors. The apprenticeship exposes students to laboratory research and development and provides a hands-on preview of a career in science and engineering. The students participate in research projects in electrical, mechanical, and aerospace engineering; physics; mathematics; or computer science.
Each student is supervised by a research scientist or engineer and given a project they will complete during the apprenticeship. In addition to these projects, the students learn about the different types of research done at ARL:UT through presentations from ARL:UT staff, a tour of ARL:UT's Lake Travis Test Station, and a chance for the students to showcase their research through technical reports and poster presentations.
The apprenticeship began as a part of the Department of Defense Science and Engineering Apprenticeship Program for High School Students and encourages students to pursue careers in the science and engineering disciplines, particularly in areas related to the needs of the U.S. Department of Defense. ARL:UT accomplishes this goal by carefully assigning each student to a research project that can be completed during the summer. When the program started, in the summer of 1982, nine students from five local high schools participated. Now, over 561 students have taken part in the program, and most have gone on to major in science or engineering in colleges throughout the United States. Many participants return to ARL:UT in student and research positions and stay on to contribute for several years.

2018 Student Projects

The Holloman High Speed Test Track is a 16-kilometer rail track developed to test the effects of velocities upward of Mach 8 on test items such as aircraft and missiles. These high speeds require the track rails to be aligned with submillimeter precision to prevent catastrophic damage to the rails and test items. To measure any misalignment introduced over time, surveyors must first place GPS receivers every 15 meters along the track at 1000 points called “benchmarks” to collect GPS observations. These data are processed to compute the position of each benchmark. Keeping accurate records for over 1000 benchmarks is a colossal task, motivating the creation of a mobile application that allows surveyors to accumulate and share information regarding GPS data collection via their mobile devices. The app tracks each receiver’s data collection in real time and allows users to add field notes for future reference. The device’s built-in clock and location services autopopulate the position and time. By creating reliable records and providing real-time updates, the app reduces the probability of user error. In order to facilitate the synchronicity of data among connected surveyors, the app establishes a wireless peer-to-peer (P2P) data-sharing protocol. With this data-sharing scheme, the data is not centrally stored but shared among all peers, increasing the robustness of the data collection. The P2P network is flexible, allowing disconnections, reconnections, and dozens of connected peers. Because of this flexibility, this project can be extended to other manual data-collection applications within the survey community and other fields.  
Research poster
Space and Geophysics Laboratory
Numerical electromagnetics code (NEC) is an electromagnetic method of moments modeling software commonly used to simulate antennas and antenna systems. NEC itself is a compiled executable with a card-style input file and tabular output file. The output file contains thousands of lines of numbers that are challenging for the human eye to analyze. This project encompasses the development of a 3D visualization tool that will make NEC output easier to interpret and allow custom views. The tool is written in Python to enable simple integration and expansion into a larger antenna-modeling framework. For 3D visualization, this program uses the module Mayavi2, which does 3D plotting and allows users to manipulate and save custom views of 3D objects. The developed tool displays output data, such as antenna gain, overelevation, azimuth, and frequency, making common visuals, such as elevation and azimuthal cuts, trivial to examine. With this data visualization tool, users will be able to easily analyze their NEC model output.
Research poster
Space and Geophysics Laboratory
Advanced persistent threat (APT) groups present significant challenges to the integrity and security of cyberinfrastructure. These groups routinely use remote access toolkits (RATs) to exercise control over their victims’ machines for the purpose of surveillance and data exfiltration. The objective of this project was to enrich security information and event management (SIEM) data sources to automatically link phases of RAT-based APT activity. The project emphasized the use of CASCADE, an application that analyzes system or network data and identifies relationships between events based on built-in analytics. For this project, we created a network of virtual machines to represent a typical APT actor (red) and victim (blue) profiles. The red machines housed a suite of RATs and other penetration testing tools, and the blue workstations forwarded Sysmon, security, and application-specific logs to a remote CASCADE log server. We conducted tailored exploitation and collection operations to emulate specific APT methodologies—namely, APT3. These operations leveraged the capabilities of installed RAT software and penetration-testing tools. Based on the results of our analysis, we created new SIEM and CASCADE analytics to increase the automation of RAT detectors and to link the phases of cyber intrusions. In addition, we successfully leveraged these analytics to provide decision support for “hacking back” RAT server exploitation.
Research poster
Signal and Information Sciences Laboratory