Postdoctoral Appointee - Radiochemical Separations, Chemical Process Engineering
About the job
- A Ph.D. in Radiochemistry, Chemistry, Chemical Engineering, or a closely related field, completed within the past five years or soon to be completed.
- Proof of U.S. citizenship is required to comply with federal regulations and contract requirements.
- Experience designing, building, and testing experimental setups for chemical or related process research.
- Knowledge of aqueous separations chemistry, radiochemical methods, or nuclear fuel cycle-related chemical processes.
- Experience with one or more separation methods such as solvent extraction, ion exchange, adsorption, precipitation, or related unit operations.
- Experience with instrumentation, sensors, data acquisition, and control systems for laboratory-scale process equipment.
- Ability to independently plan and execute experimental research.
- Strong written and verbal communication skills.
- Ability to work effectively in a collaborative, multidisciplinary research environment.
- Ability to model the organization’s core values of impact, safety, respect, integrity, and teamwork.
- Plan and execute radiochemical laboratory experiments involving radiotracers, irradiated targets and materials, and aqueous separations in controlled environments such as radiological hoods, gloveboxes, and hot cells.
- Design, assemble, operate, troubleshoot, test, and optimize experimental systems and specialized apparatus, including fluid handling systems, centrifugal contactors, sample preparation methods, and instrumentation, to support advanced aqueous recycling of used nuclear fuel.
- Evaluate separations performance for actinides, lanthanides, and fission products through experimental testing and data analysis.
- Prepare test plans and operating procedures, and contribute to technical documentation, reports, publications, and sponsor deliverables.
- Collaborate with multidisciplinary teams and maintain a strong commitment to safe work practices and compliance with laboratory requirements.
- Author or co-author research proposals and white papers focused on bringing new research and development opportunities to the division.
- Contribute to the development and optimization of advanced aqueous recycling options for used nuclear fuel and related separations flowsheets, with an emphasis on hands-on experimental work in radiochemistry laboratory environments.
- Support the development and optimization of aqueous processing approaches and dry head-end separations methods.
- Evaluate process performance.
- Hands-on experience working with radiotracers, radioactive isotopes, irradiated targets, or related laboratory-scale radiochemical studies is preferred; alternatively, demonstrated aptitude to develop these capabilities through training based on strong hands-on chemistry or chemical engineering laboratory experience.
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
Professional Development Budget
Wellness Program
Growth & Insights and Company News
6 month growth
1%1 year growth
0%2 year growth
1%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.
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.