Graduate Summer Intern – Computational Synthesis of PGM-free Fuel Cell Catalysts

National Laboratory of the Rockies • Golden, CO

1d • Onsite

About The Position

The Scalable Algorithms, Modeling, and Simulation group in the NLR Computational Science Center has an opening for a graduate student researcher in computational synthesis, with a particular emphasize on machine-learned interatomic potentials (MLIPs). The researchers will train MLIPs, assess their accuracy and apply them to studying the relevant reaction energetics for synthesis of Fe-N-C fuel cell catalysts using automated workflows. In conjunction with a team of staff scientists, different synthetic precursors will be identified for subsequent synthesis. We are looking for a dynamic, motivated researcher with a strong technical background and an interest in the mission of NLR. The successful candidate will collaborate with NLR staff and researchers to train and deploy MLIPs for the optimization of Fe-N-C catalyst synthesis.

Requirements

• Minimum of a 3.0 cumulative grade point average.
• Undergraduate: Must be enrolled as a 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. Eligible for an internship period of up to one year.
• Graduate: Must be enrolled as a full-time student in a master’s degree program from an accredited institution.
• Post Graduate: Earned a master’s degree within the past 12 months. Eligible for an internship period of up to one year.
• Graduate + PhD: Completed master’s degree and enrolled as PhD student from an accredited institution.
• Applicants are responsible for uploading official or unofficial school transcripts, as part of the application process.
• If selected for position, a letter of recommendation will be required as part of the hiring process.
• Must meet educational requirements prior to employment start date.
• Excellent written and verbal communication skills.
• Excellent knowledge of chemistry, including molecular dynamics and reaction modeling
• Experience with the MACE-based foundational models and their fine-tuning

Nice To Haves

• Knowledge of workflow automation, including the use of AI for automated analysis of data.
• Experienced running density functional theory (DFT) software packages such as JDFTx for VASP.

Responsibilities

• Fine-tuning of foundational model MLIPs with domain-specific data and assessment of their accuracy
• Collaborate with NLR researchers to use the trained MLIPs to study the chemistry of Fe incorporation into N-C sites for different synthetic precusors
• Develop automated workflows in conjunction with NLR and collaborating institutions, including automated analysis using AI
• Process and visualize results from simulations provide chemical insight and intuition into important chemical reactions.
• Author, present and assist in the preparation of technical papers, reports and conference proceedings on topics related to MLIP studies of the synthesis of Fe-N-C fuel cell catalysts.

Benefits

• medical, dental, and vision insurance
• 403(b) Employee Savings Plan with employer match
• sick leave (where required by law)
• performance-, merit-, and achievement- based awards that include a monetary component
• relocation expense reimbursement

What This Job Offers

• Job Type: Full-time
• Career Level: Intern