October 8, 2024

DOE Lab Internships and Beyond: Student Pathways

DOE National Laboratories across the United States have been leaders in scientific innovation for more than 60 years - and they hire students just like you every summer to work on meaningful research contributions.   

There are dozens of pathways to a DOE internship, which offer competitive compensation for 10-12 weeks in different cities nationwide. Understanding all the opportunities and differences between labs alone can be overwhelming, let alone finding an internship program where you are eligible. This session aims to demystify which opportunities are available for you, regardless of whether you are a domestic or international student, an undergraduate or graduate student, for nearly any STEM discipline. The opportunities we will discuss in this workshop allow you to work on real-world projects that often lead to publications and presentations while you get to explore a new city for a whole summer.   

Whether your interests are renewable energy, data science, advanced computing, cybersecurity, or something else, DOE labs allow you to contribute to cutting-edge research while building your career. Don't let these hard-to-find opportunities pass you by! Get curated tips on how you can be selected to become part of the next generation of scientific innovators. The session speakers, who have previously interned at DOE national labs, will share their personal advice regarding application strategies, what to expect during the internship, and how to make the most of it. During the presentation, students are encouraged to start applying!

About the speaker

Mrs. Kristen Hallas is a 3rd year doctoral student and 1st generation scholar attending the University of Texas Rio Grande Valley (UTRGV).  In Spring 2022, she earned a Bachelor of Science in Applied Mathematics with a minor in Computer Science at UTRGV, graduating Summa Cum Laude with the Highest Distinction in Honors Studies. The following fall, she began pursuing her PhD in Mathematics and Statistics with Interdisciplinary Applications (MSIA). Over the last three summers, she has worked at national labs to learn more about High Performance Computing (HPC) systems. She interned with the HPC Security Analytics & Monitoring Group at Oak Ridge National Lab in 2022, designing an interactive Python visualization that explores the power and cooling trends of HPC system nodes in relation to their physical location in the data center. In 2023, she worked as a Technical Research Aide for the Mathematics and Computer Science Division at Argonne National Lab, improving a Node.js app that renders visual analytics about the performance of simulated HPC networks. In 2024, she joined the Applied Statistics Group at Lawrence Livermore National Lab, building neural network architecture (optimized on state-of-the-art HPC systems) that can generate equation-of-state tables and predict phase maps with an overall accuracy of ~97%. Thanks to the National Nuclear Security Administration (NNSA) MSIIP program, she will continue collaborating with Livermore throughout the 2024-2025 academic year on functional generalized robotics for alloy discovery. After graduating, she hopes to keep building systems towards positive aims (like reaching a net-zero emission economy or safeguarding cyber assets) on a team dedicated to advancing scientific progress. She dreams of giving back the mentorship graciously given to her throughout her academic journey.

Ms. Martha Asare is a Computer Science with Interdisciplinary Applications (CSIA) PhD student in Computer Science at the University of Texas Rio Grande Valley (UTRGV), supervised by Dr. Jinghao Yang. Her research is focused on machine vision systems for metal 3D printing utilizing advanced machine learning algorithms to innovate additive manufacturing. Martha holds a bachelor's degree in Statistics from Kwame Nkrumah University of Science and Technology, Ghana, and a master’s degree in Applied Statistics and Data Science from UTRGV. Her expertise includes handling large datasets and improving predictive modeling. Martha's academic excellence is evidenced by the Best Master’s Research Student award at UTRGV and other honors like the Outstanding Poster Award at Florence Nightingale Day 2024. She has significant experience in data analytics, as evidenced by her role at Lawrence Berkeley National Laboratory, where she worked with the Perlmutter supercomputer. Martha is proficient in technical skills such as R, Python, NLP, MATLAB, and SPSS, and she is a recognized leader and innovator in her field.