Graduate Intern: Agentic AI for Fe-N-C Catalyst Synthesis

National Laboratory of the Rockies; Golden (CO); Hybrid; USD 45,000 - 71,000; Full time

Benefits offered by this job

Medical Insurance

Dental Insurance

Vision Insurance

403(b) match

Sick Leave

Job summary

National Laboratory of the Rockies (NLR) in Golden, CO invites a graduate intern to advance agentic AI for automating MLIP training and reaction energetics in Fe‑N‑C catalyst research. The 40-hour workweek offers a hybrid on-site/home schedule and emphasizes interdisciplinary collaboration with renowned scientists.

The role requires strong academic progress in relevant fields, plus hands-on AI/ML, Python environments, and materials science exposure.

Qualifications

• Minimum of a 3.0 cumulative GPA.
• Undergraduate: full‑time student in a bachelor’s degree program from an accredited institution.
• Post Undergraduate: earned a bachelor’s degree within the past 12 months.
• Graduate: enrolled as a full‑time student in a master’s degree program from an accredited institution.

Responsibilities

• Develop agentic AI framework that automates training of MLIPs and explores reaction energies for Fe‑N‑C catalyst synthesis.
• Collaborate with researchers to study graphitization and Fe incorporation into the N-C matrix for different precursors.
• Develop AI that processes, analyzes, and visualizes simulation results to provide chemical insight.
• Prepare and contribute to technical papers and reports on AI‑MLIP studies of Fe‑N‑C catalyst synthesis.

Skills

Communication skills

AI automation

Chemistry knowledge

Python environments

Education

Bachelor’s degree program enrolled

Master’s degree program enrolled

PhD student

Tools

LangChain

Job description

National Laboratory of the Rockies (NLR) in Golden, CO invites a graduate intern to advance agentic AI for automating MLIP training and reaction energetics in Fe‑N‑C catalyst research. The 40-hour workweek offers a hybrid on-site/home schedule and emphasizes interdisciplinary collaboration with renowned scientists.

The role requires strong academic progress in relevant fields, plus hands-on AI/ML, Python environments, and materials science exposure.