Postdoctoral Researcher – Agentic AI-Driven Computational Synthesis of PGM-free Fuel Cell Catalysts

Advanced Energy Systems - Colorado School of Mines & NLR

. Develop an agentic AI framework that automates training of machine-learned interatomic potentials (MLIPs) and exploration of reaction combinations and energetics relevant to Fe-N-C catalyst synthesis .

Posted 10/28/2026full-timeGolden • Colorado • United StatesMid-LevelSenior💰 $76,600 - $126,400 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI automation and machine learning applications within chemistry and materials science, with a strong focus on developing frameworks for interatomic potentials and catalyst synthesis. Proficient in Python for AI/ML workflows and familiar with key software packages used in materials science.

Highest-signal resume keywords

AI AutomationPython Environment CreationMachine-Learned Interatomic PotentialsDFT Software PackagesChemistry and Materials Science

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI AutomationPython ProgrammingMachine LearningInteratomic PotentialsGraphitization ChemistryDFT SoftwareLAMMPSQ-ChemGaussianMACE-based Models

Soft Skills

Excellent Written CommunicationExcellent Verbal Communication

Tools & Technologies

LangChainJDFTxVASPAI Tools

Certifications & Qualifications

Personal Identity Verification (PIV) Card

Industry Keywords

Fe-N-C Catalyst SynthesisZeolitic Imidazolate Framework (ZIF)Chemical InsightSimulation Results Visualization

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Develop an agentic AI framework that automates training of machine-learned interatomic potentials (MLIPs) and exploration of reaction combinations and energetics relevant to Fe-N-C catalyst synthesis
Collaborate with NLR researchers to study graphitization chemistry and iron incorporation into the N-C matrix for different synthetic precursors, including iron salts and zeolitic imidazolate framework (ZIF) linkers
Develop AI that processes, analyzes, and visualizes simulation results to provide chemical insight into important reactions
Author, present, and assist with technical papers, reports, and conference proceedings on AI-automated MLIP studies of Fe-N-C fuel cell catalyst synthesis
Collaborate with NLR staff and researchers to optimize Fe-N-C catalyst synthesis

Requirements

What you’ll need

Recent PhD graduate within the last three years
Must meet educational requirements prior to employment start date
Excellent written and verbal communication skills
Excellent knowledge of AI automation, especially creation and use of skills files and deterministic frameworks such as LangChain
Experience with chemistry and materials science, especially incorporating ML/AI into workflows
Experience creating Python environments for AI/ML tasks and workflows
Interest in building materials science and chemistry software packages, such as LAMMPS, Q-Chem, and Gaussian, including using AI tools in the building process
Experience running DFT software packages such as JDFTx or VASP preferred
Experience with MACE-based machine-learned interatomic potentials, especially foundational models preferred
Ability to obtain and maintain a federal Personal Identity Verification (PIV) card, including a favorable background investigation
Must pass a pre-employment drug test

Benefits

Comp & perks

Medical, dental, and vision insurance
Short-term disability insurance
Pension benefits
403(b) Employee Savings Plan with employer match
Life and accidental death and dismemberment (AD&D) insurance
Personal time off (PTO) and sick leave
Paid holidays
Performance-, merit-, and achievement-based awards with a monetary component may be available
Relocation expense reimbursement may be available for some positions
Robust professional development opportunities