Summer 2026
Database Operations & Reporting Intern
Posted on 5/16/2026
Argonne National Laboratory
1,001-5,000 employees
Advanced scientific research and computing facilities
Compensation Overview
$19.55 - $24.44/hr
No H1B Sponsorship
Woodridge, IL, USA
In Person
US Citizenship Required
 Data & Analytics (2) |
|---|
| , |
- Currently enrolled in undergraduate studies at an accredited institution; Graduated from an accredited undergraduate institution within the past 3 months.
- Must be 18 years or older at the time the appointment begins.
- Must possess a cumulative GPA of 3.0 on a 4.0 scale.
- Must be a U.S. citizen or Legal Permanent Resident at the time of application.
- If you accept an offer, you must pass a screening drug test.
- The entire appointment must be conducted within the United States.
- Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne’s Legal Department.
- All Argonne offers for appointments in the student employment category are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis.
- Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.
- Under the direction of a mentor, the intern will assist with the execution and testing of SQL scripts used for routine operational updates.
- Support validation of report outputs.
- Help verify data consistency between FMax and related reporting tools.
- Work with live production data in a controlled environment to support recurring reporting processes.
- Document script logic and data checks.
- This project provides hands-on experience with SQL scripting, enterprise data maintenance, and operational reporting workflows.
- None
Argonne National Laboratory advances scientific discovery and sustainability by providing access to large-scale research facilities and high-performance computing for government, academia, and industry partners. Researchers use the Advanced Photon Source for atomic-level materials studies and the Argonne Leadership Computing Facility for complex simulations and data analysis. The lab differentiates itself through its shared-use, multi-institution partnerships and a focus on eco-innovation, net-zero goals, and AI accelerator development. Its aim is to address real-world energy, materials, and data science challenges by combining cutting-edge infrastructure with collaborative research efforts.
Company Size
1,001-5,000
Company Stage
Grant
Total Funding
$19.7M
Headquarters
Lemont, Illinois
Founded
1946
Simplify's Take
What believers are saying
- AI inference service monetizes spare supercomputer capacity for DOE researchers.
- GridMind targets utility operators with explainable natural-language control-room support.
- Polybot accelerated materials discovery from 4,300 combinations to 64 experiments.
What critics are saying
- Federal appropriations pressure can defer facility upgrades and staffing.
- Commercial AI and utility software vendors can outcompete research prototypes.
- Accelerator procurement delays can slow AI capability upgrades and user access.
What makes Argonne National Laboratory unique
- DOE multidisciplinary lab with >1,000 scientists and major user facilities.
- Operates Advanced Photon Source and Argonne Leadership Computing Facility.
- Combines industry, academia, and government collaborations on national challenges.
Help us improve and share your feedback! Did you find this helpful?
Your Connections
People at Argonne National Laboratory who can refer or advise you
Benefits
Health Insurance
Dental Insurance
Vision Insurance
Life Insurance
Disability Insurance
Paid Vacation
Paid Sick Leave
Paid Holidays
Remote Work Options
Flexible Work Hours
401(k) Retirement Plan
401(k) Company Match
Professional Development Budget
Wellness Program
Growth & Insights and Company News
6 month growth
↑ 0%1 year growth
↑ 1%2 year growth
↑ 2%Researchers at the US Department of Energy's Argonne National Laboratory have developed GridMind, an agentic AI system designed to assist power grid operators through natural language interaction. The system functions as a reasoning co-pilot for control rooms, simplifying complex grid management tasks. GridMind employs a multi-agent architecture where specialised AI agents handle different functions, such as power scheduling and weather-based contingency planning. Large language models coordinate these agents to analyse situations, reason across different tasks and provide explainable recommendations. The system transforms technical analysis into conversational support whilst maintaining rigorous accuracy. Tests on standard power grid models demonstrated that GridMind consistently produced correct results across multiple state-of-the-art language models. The technology aims to accelerate decision-making by integrating disconnected workflows into a coherent reasoning engine.
A research team led by the US Department of Energy's Argonne National Laboratory has developed an AI adviser that optimises machine learning algorithms during autonomous experiments, accelerating discovery of advanced electronic materials. The system was applied to Polybot, Argonne's AI-guided robotic laboratory, to investigate mixed ion-electron conducting polymers for wearable electronics and energy storage. The adviser evaluates algorithm performance in real time and communicates insights to scientists who refine experimental plans. It reduced the study to just 64 experiments from over 4,300 possible combinations. During testing, the adviser suggested switching AI algorithms, leading to significant performance improvements, and identified deposition speed as a key performance driver. The research was published in Nature Chemical Engineering and included collaborators from the University of Chicago, Lawrence Berkeley National Laboratory and other institutions.
米国エネルギー省（DOE）のアルゴンヌ国立研究所の研究者らは、ローレンスバークレー国立研究所が主導するモビリティシステムの設計・計画に関するプロジェクトの一環として、交通状況を予測するAIシステムを開発中だ。