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National Laboratory of the Rockies

Renewable energy research and data services

Agentic AI-Driven Computational Synthesis of PGM-free Fuel Cell Catalysts Intern

Quantitative Biology & BiostatisticsConfirmed live in the last 24 hours
InternshipGolden, CO, USA$21.39 - $34.23 /hr

About the Job

Requirements

  • Maintain a minimum cumulative grade point average of 3.0.
  • Meet the student or recent-graduate eligibility requirements applicable to the undergraduate, post-undergraduate, graduate, or graduate-plus-PhD internship path, including enrollment in or recent completion of the specified degree program.
  • Have excellent written and verbal communication skills.
  • Have excellent knowledge of AI automation, including skills files and deterministic frameworks such as LangChain.
  • Have experience with chemistry and materials science, especially incorporating machine learning or artificial intelligence into workflows.
  • Have experience creating Python environments for AI and machine learning tasks and workflows.

Responsibilities

  • Develop an agentic artificial intelligence framework that automates training machine-learned interatomic potentials and exploration of the combinatorial space of reactions relevant to Fe-N-C catalyst synthesis and their energetics.
  • Collaborate with NLR researchers to use the developed artificial intelligence to study graphitization chemistry and iron incorporation into the nitrogen-carbon matrix for different synthetic precursors, such as iron salts and zeolitic imidazolate framework linkers.
  • Develop artificial intelligence that processes, analyzes, and visualizes simulation results to provide chemical insight into important chemical reactions.
  • Author and present technical papers, reports, and conference proceedings, and assist in their preparation, on artificial-intelligence-automated machine-learned interatomic-potential studies of Fe-N-C fuel-cell-catalyst synthesis.

Desired Qualifications

  • Have experience with artificial-intelligence workflow automation of machine-learned interatomic-potential calculations to explore chemical space relevant to Fe-N-C catalyst synthesis.
  • Have experience running density functional theory software packages such as JDFTx or VASP.
  • Have experience with MACE-based machine-learned interatomic potentials, especially foundational models.

About the Company

National Laboratory of the Rockies

Renewable energy research and data services

NREL analyzes and develops renewable energy and energy efficiency technologies and provides energy data and research services to government agencies, private companies, and universities. Its offerings include research outputs, data products, and Web services through a developer network that gives clients access to renewable-energy data and information on alternative fuels. The work is funded mainly by government grants and partnerships, and NREL uses its findings to help clients create sustainable energy solutions and policy guidance. The organization differs from many peers by being a government-supported national lab with large, open data resources and a focus on practical deployment, standards, and collaboration across public and private sectors. Its goals include accelerating the adoption of clean energy, advancing energy efficiency, reducing carbon emissions, promoting circular economy practices and decarbonizing its own operations to reach net-zero emissions by 2050.

Company size
1,001-5,000
Company stage
Grant
Total funding
$14.6M
Headquarters
Golden, CO
Founded
1977

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