The Physical Sciences and Engineering Directorate seeks an outstanding Scientist to lead and support frontier research at the intersection of AI, autonomous platforms, data infrastructure, and materials science and/or chemistry. The candidate will have established expertise in materials or chemistry using autonomous platforms and AI to accelerate their research, with the expectation of forming an independently-funded research program within 2-3 years. The successful candidate will develop and apply advanced data-driven methodologies to accelerate discovery in materials/chemistry design, characterization, and synthesis.

Focus Areas (expertise in one or more is highly desirable)

Autonomous laboratories for materials synthesis and characterization
AI/ML for predictive modeling and inverse design
Generative models, reinforcement learning, and agent-based approaches to streamline experimentation and accelerate discovery
Integration of HPC, data infrastructure, and ML pipelines for data-driven and autonomous research
Digital twins and simulation-augmented AI tools

Key Responsibilities

Develop and lead an independent and collaborative research program in materials science or chemistry using AI and autonomous labs aligned with Argonne’s and the DOE’s missions
Leverage existing facilities in autonomous labs, with the freedom to build other novel platforms
Publish in refereed journals and present at conferences, symposia, and seminars
Provide work direction and mentorship to postdoctoral appointees, research assistants, students, and technical staff
Execute all activities in compliance with Argonne’s ES&H policies, Safeguards and Security policies, work rules, and safe practices

Position Requirements

Ph.D. in Materials Science, Physics, Chemistry, Computer Science, or a related field
Proven research track record in materials science or chemistry using autonomous platforms and AI/ML
Demonstrated ability to formulate scientific problems relevant to the DOE portfolio
Strong oral and written communication skills, with the ability to work effectively with internal and external collaborators to achieve established goals
Demonstrated ability to collaborate in a multidisciplinary environment and provide scientific guidance to a diverse research community
Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork

Submit the following materials as attachments to your application:

Cover letter detailing how your experience and expertise align with and will contribute to this position
Curriculum vitae with publication list and contact information for three professional references
1-page research statement outlining proposed research directions

RD3: Bachelors and 8+ years of experience, Masters and 5+ years, PhD and 4+ years, or equivalent

The expected hiring range for this position is $116,250 - $181,350 annually

RD4: Bachelors and 12+ years of experience, Masters and 7+ years, PhD and 6+ years, or equivalent

The expected hiring range for this position is $ 138,000 - $ 215,280 annually

Job Family

Research Development (RD)

Job Profile

Materials/Ceramics/Metallurgical 3

Worker Type

Regular

Time Type

Full timeThe expected hiring range for this position is $116,250.00 - $181,350.00.

Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

Click here to view Argonne employee benefits!

As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.

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 of employment 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.

About the company

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

5,001-10,000

Company Stage

Grant

Total Funding

$19.7M

Headquarters

Lemont, Illinois

Founded

1946

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Simplify's Take

What believers are saying

UChicago Argonne LLC won a renewed DOE contract in 2026 for five years.
2026 launch of ChemGraph, GridMind, and AI inference services deepens Argonne's relevance.
Activated Materials Lab opened in 2026, expanding nuclear fuels research and user-facility demand.

What critics are saying

DOE's FY2027 request cuts Argonne funding about $155.8 million, hitting programs and construction.
2025 buyouts showed staff pressure; more layoffs follow if Congress accepts budget cuts.
A prolonged federal reset or shutdown threatens APS, Aurora utilization, and Argonne's operating model.

What makes Argonne National Laboratory unique

Aurora and APS create a rare AI-plus-X-ray discovery stack for 2026 science.
Chain Reaction Innovations embeds startups inside Argonne with facilities, mentors, and DOE funding.
Q-NEXT and other DOE centers keep Argonne central to U.S. quantum 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

Growth & Insights and Company News

6 month growth

1 year growth

2 year growth

The Register

Oct 7th, 2026

Argonne's AI X-ray nanoprobe beamline responds to natural language commands to analyse samples

Scientists at Argonne National Laboratory have developed an AI-enhanced X-ray microscope that researchers can control using natural language commands. The system combines an agentic AI platform with a hard X-ray nanoprobe beamline, allowing users to simply ask the microscope to examine specific areas of interest. The technology addresses a major challenge in X-ray imaging: modern experiments can generate millions of images in hours, requiring days of conventional analysis. Argonne's AI system reconstructs images in real time as data arrives. The microscope can autonomously collect data, process results, and identify additional areas requiring investigation. Computational scientist Mathew Cherukara said the AI-enhanced microscope could benefit numerous scientific fields beyond its initial microelectronics demonstration. The project forms part of the SYNAPS-I initiative, led by Lawrence Berkeley National Laboratory under the Department of Energy's Genesis Mission, launched in November 2025.

Associated Press

Jul 7th, 2026

Argonne's ChemGraph uses AI to automate computational chemistry workflows

Researchers at the US Department of Energy's Argonne National Laboratory have developed ChemGraph, an open-source framework that uses artificial intelligence to automate computational chemistry workflows. The tool provides a natural language interface, allowing researchers to describe scientific problems in plain language, which the system then maps onto computational tasks and analyses. ChemGraph was developed using resources at the Argonne Leadership Computing Facility, including the Aurora exascale supercomputer. The framework uses AI agents to handle different workflow tasks, from planning to execution and data aggregation. It calls appropriate scientific tools to reduce the risk of AI hallucination, using AI to run physics-based simulations rather than relying solely on existing knowledge. The framework supports applications in combustion efficiency, critical materials, and next-generation batteries, and complements DOE's Genesis Mission to accelerate science through AI.

The Register

May 27th, 2026

Argonne National Laboratory launches private AI inference service using spare supercomputer capacity

The US Department of Energy's Argonne National Laboratory has launched an AI inference service using spare supercomputing capacity to support researchers across DoE labs and the Genesis Mission. The service runs on two clusters: Sophia, with 192 Nvidia A100 GPUs, and Metis, featuring 32 SambaNova SN40L AI accelerators. The platform provides secure access to various large language models, including OpenAI's GPT-OSS, Google's Gemma, and Meta's Llama, through a chatbot-like portal. Researchers are using the service to analyse experimental data in real time, including predicting plasma disruptions in fusion energy research and processing data from particle accelerators and telescopes. Argonne plans to extend the service to its Nvidia GH200-based Tara and B200-based Minerva systems, enabling scientists to experiment with AI without building their own infrastructure.

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.

Argonne National Laboratory

PSE Autonomous Discovery Scientist

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