Full-Time

Postdoctoral Appointee

Energy Systems

Posted on 2/21/2026

Argonne National Laboratory

Argonne National Laboratory

1,001-5,000 employees

Advanced scientific research and computing facilities

Compensation Overview

$72.9k - $121.5k/yr

Company Does Not Provide H1B Sponsorship

Woodridge, IL, USA

In Person

Category
Data & Analytics (1)
Required Skills
Python
Julia
C/C++
Requirements
  • Recent or soon-to-be completed (typically within the last 0-5 years) Ph.D. in engineering, operations research, computer science, applied mathematics, or a related field.
  • Demonstrated expertise in mathematical optimization techniques and solvers (e.g., Gurobi, CPLEX), including linear, mixed-integer, or stochastic programming.
  • Proficiency in at least one modern scientific programming language (Python, Julia, or C++ preferred).
  • Knowledge of power system operations, electricity market mechanisms, and energy system modeling.
  • Analytical and problem-solving skills, with the ability to work independently and collaboratively in a team environment.
  • Proven research record, evidenced by peer-reviewed publications and conference presentations.
  • Skilledwritten and verbal communication skills at all levels of the organization.
  • Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.
  • This position requires an on-site presence at the Argonne campus in Lemont, Illinois.
Responsibilities
  • Develop and implement power system modeling tools, with emphasis on simulation and optimization of hydropower systems and their integration with broader electricity markets.
  • Conduct research on electricity system operations, planning, and market design, including evaluation of economic and environmental trade-offs.
  • Contribute to projects involving capacity expansion, production cost modeling, and equilibrium modeling of power systems.
  • Design and apply mathematical optimization models, including linear, mixed-integer, and stochastic programming.
  • Work with programming languages such as Python, Julia, or C++ to build robust analytical tools and perform large-scale data analysis.
  • Collaborate with internal teams and external stakeholders, including U.S. Department of Energy offices and other national laboratories.
  • Communicate research findings through peer-reviewed publications, technical reports, and presentations at scientific conferences.
  • Support proposal development and stay abreast of developments in the field through literature reviews and professional engagement.
Desired Qualifications
  • Experience with hydropower modeling, energy storage systems, or renewable integration.
  • Familiarity with production cost models or capacity expansion models.
  • Experience collaborating with government agencies or working in multi-institutional research environments.
  • Understanding of financial and economic evaluation of energy technologies.
Argonne National Laboratory

Argonne National Laboratory

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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 Jobs

Simplify's Take

What believers are saying

  • 50-year lithium-ion battery R&D advances energy storage capabilities.
  • Scalable solar-fuel systems compete economically with fossil fuels.
  • Bioremediation and biochips drive environmental and health innovations.

What critics are saying

  • Tesla and CATL undercut ANL's battery licensing with scaled production.
  • Open-source Llama enables utilities to build GridMind equivalents in-house.
  • DOE reallocates budgets to IRA tax credits, slashing ANL funding.

What makes Argonne National Laboratory unique

  • GridMind AI agent assists power grid operators via natural language.
  • AI adviser optimizes Polybot robotic lab for electronic materials discovery.
  • Advanced Photon Source upgrade enables unparalleled scientific research.

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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

Headcount

6 month growth

0%

1 year growth

1%

2 year growth

2%
Business Wire
Mar 26th, 2026
Argonne Lab develops GridMind AI agent to support power grid operators

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.

Yahoo Finance
Mar 11th, 2026
AI adviser helps Argonne's robotic lab discover advanced electronic materials in just 64 experiments

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.

Techable
Nov 21st, 2020
アルゴンヌ国立研究所、1万個以上のセンサーを使って交通状況を瞬時に予測!

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。

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