Full-Time
Postdoctoral Appointee
Heterogeneous Catalysis
Posted on 11/1/2025
Argonne National Laboratory
1,001-5,000 employees
Advanced scientific research and computing facilities
Compensation Overview
$70.8k - $117.9k/yr
Company Does Not Provide H1B Sponsorship
Woodridge, IL, USA
In Person
 Process Engineering |
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- Recent or soon-to-be-completed PhD (within the last 0-5 years) in the field of organic, organometallic, or inorganic chemistry, or a related field
- Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork
- Conduct research as part of a multidisciplinary team in heterogeneous Catalysis focusing on non-traditional catalysts and non-equilibrium catalysis
- Design, synthesize, and characterize well-defined complexes supported on metal oxide or non-traditional support materials
- Investigate how the stereoelectronic properties of the catalytic surface and/or external stimuli manipulate catalyst electronic properties, and how these factors can manipulate catalytic mechanisms and reaction outcomes
- Perform detailed in situ / operando studies of catalysts using Infrared and X-ray Absorption Spectroscopies, as well as other advanced spectroscopic techniques
- Experience in the synthesis and evaluation of molecular and/or heterogeneous catalysts
- Strong understanding of kinetics and thermodynamics as applied to catalysis
- Knowledge of and experimental expertise in synthesis and characterization of highly air-sensitive catalyst materials
- Experience with the design and construction of batch and flow reactors for catalysis
- Proficiency with analytical techniques such as X-ray absorption spectroscopy, nuclear magnetic resonance spectroscopy, infrared and ultraviolet spectroscopy, as well as experience with high resolution STEM imaging
- Excellent written and oral communication skills
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
- 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
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。