Ames National Laboratory Scientist I
About The Position
The successful candidate will serve as the computational lead within an integrated discovery platform for critical materials separation. The role involves architecting digital frameworks—including graph neural networks, molecular representations, thermodynamic equilibrium modeling, and multi-agent LLM systems—to interpret complex analytical data, predict extractant-metal selectivity, and drive AI-guided open-loop and closed-loop experimental studies through direct interaction with autonomous robotic laboratories. The position requires a strong foundation in coordination chemistry, solution thermodynamics, and aqueous electrolyte equilibria, along with advanced programming capabilities and experience in scientific machine learning, agentic LLM workflows, Linux-based HPC environments, and autonomous laboratory systems. The candidate will collaborate with multi-institutional teams to translate physical sciences into computational workflows. This is a one-year term appointment at Ames National Laboratory in Ames, Iowa.
Requirements
- PhD or equivalent combination of education and experience.
- Strong fundamental grasp of coordination chemistry, solution thermodynamics, and aqueous electrolyte equilibria.
- Advanced programming proficiency in Python, RDKit, and scientific machine learning frameworks such as PyTorch Geometric.
- Hands-on experience developing LLM-based agentic workflows.
- Experience navigating Linux-based HPC cluster environments.
- Experience interfacing with autonomous robotic laboratories for open-loop and closed-loop studies.
- Ability to work collaboratively in multi-institutional teams.
Nice To Haves
- Experience running aqueous equilibrium engines, specifically PHREEQC or phreeqpy, and familiarity with non-ideal electrolyte models such as Pitzer equations, Bromley, or e-NRTL.
- Prior exposure to wet-chemical laboratory environments handling mineral acids, metal salt solutions, and organic solvent diluents.
- Experience interfacing with autonomous robotic labs or automated liquid handlers for AI-guided open-loop and closed-loop experimental studies.
- Familiarity with interpreting datasets from ICP-MS, ICP-OES, UV-Vis spectrophotometry, or ATR-FTIR.
- Basic familiarity with OpenCV, YOLOv8, or simple image-segmentation tools.
- Exposure to real-world hydrometallurgical feedstocks such as acid rock drainage, zinc roaster residues, or bauxite red mud.
- Experience drafting research proposals or participating in grant writing.
Responsibilities
- Serve as the computational lead within an integrated discovery platform.
- Architect digital frameworks involving graph neural networks, molecular representations, thermodynamic equilibrium modeling, and multi-agent LLM systems.
- Interpret complex analytical data and predict extractant-metal selectivity.
- Drive AI-guided open-loop and closed-loop experimental studies through direct interaction with autonomous robotic laboratories.
- Collaborate with multi-institutional teams to translate physical sciences into computational workflows.
Benefits
- Medical insurance
- Dental insurance
- Retirement benefits, including defined benefit and defined contribution plans
- Vacation, holiday, and sick time and leave plans
- Onsite childcare in Ames, Iowa
- Life insurance
- Long-term disability
- Flexible Spending Accounts
- Voluntary benefits and discounts
- Employee Assistance Program
- Wellbeing program
- WorkFlex options for some positions, offering flexibility on when, where, and how work is done
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree
