Full-Time
Autonomous Infrastructure and Robotic Science Lead
Argonne National Laboratory
5,001-10,000 employees
Advanced scientific research and computing facilities
Compensation Overview
$148.1k - $231.1k/yr
Company Does Not Provide H1B Sponsorship
Lemont, IL, USA
In Person
PhD
 Academic & Institutional Research |
|---|
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- A minimum of a Ph.D. in Computer Science, Materials Science, Physics, Chemistry, or a related field and 4+ years of experience, or equivalent.
- A proven research track record in deploying automated and autonomous platforms and artificial intelligence/machine learning toward accelerating science.
- Demonstrated ability to formulate scientific problems relevant to the Department of Energy 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.
- Evaluate the performance of assigned staff and recommend professional development, salary actions, and promotions.
- Play a key role in the recruitment and selection of high-quality staff.
- Report on research progress and new initiatives to division management, review committees, and funding agencies.
- Provide supervisory oversight, including developing, motivating, and leading a team of professionals.
- Guide the development of infrastructure for laboratory autonomy, including physical autonomous laboratories, robotics laboratories, and software frameworks for autonomous science and robotics.
- Facilitate collaborations between the Rapid Prototyping Laboratory and teams at partner institutions developing autonomous science and robotics infrastructure.
- Guide the Rapid Prototyping Laboratory team toward advancing laboratory autonomy and robotics.
- Publish in refereed journals and present at conferences, symposia, and seminars.
- Provide work direction, supervisory oversight, mentorship, development, and motivation to postdoctoral appointees, research assistants, students, and professional technical staff.
- Execute all activities in compliance with Argonne’s environment, safety, and health policies, safeguards and security policies, work rules, and safe practices.
- Expertise in autonomous laboratories for chemistry, materials, biology, or related domains.
- Experience with artificial intelligence and machine learning for predictive modeling and inverse design.
- Experience with generative models, reinforcement learning, and agent-based approaches to streamline experimentation and accelerate discovery.
- Experience integrating high-performance computing, data infrastructure, and machine learning pipelines for data-driven and autonomous research.
- Experience with digital twins and simulation-augmented artificial intelligence tools.
- Preferred experience leading and/or managing others from students to professional staff.
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
Professional Development Budget
Wellness Program
Growth & Insights and Company News
6 month growth
↓ -2%1 year growth
↓ -2%2 year growth
↓ -2%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 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.
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.
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