Assistant Computational Chemist / Chemical Engineer Catalysis

The Chemical Sciences and Engineering Division at Argonne National Laboratory invites applications for a regular, full-time Assistant Computational Chemist / Chemical Engineer position. The successful candidate will lead and contribute to computational research in electrocatalysis and heterogeneous catalysis, working closely with experimental collaborators to advance fundamental understanding and catalyst design.

This role involves conducting multiscale modeling, spectroscopy simulations, and the development of machine learning methods and automated workflows for multi-fidelity, multiscale, and multiphysics simulations. The research will be closely integrated with corresponding experimental efforts.

Key Responsibilities

• Perform computational studies in electrocatalysis and heterogeneous catalysis

• Develop and apply multiscale modeling approaches to catalytic systems

• Conduct spectroscopy simulations, including techniques such as XANES, EXAFS, and Mössbauer spectroscopy

• Develop and implement machine learning methods and automated workflows for complex catalytic simulations

• Collaborate closely with experimental researchers to interpret results and guide catalyst development

• Contribute to proposal development and funding applications

• Mentor postdoctoral researchers and graduate students

Position Requirements

• Ph.D. in physical chemistry, inorganic chemistry, computational materials science, chemical engineering, or a related field, along with 3–6 years of postdoctoral research experience

• Comprehensive understanding of quantum mechanics and catalysis

• Extensive experience in heterogeneous thermal catalysis and electrocatalysis, including:

  • catalyst design

  • mechanistic studies

  • microkinetic modeling

  • reactor modeling

• Strong computational expertise in applying quantum mechanical methods to determine electronic structure, catalytic properties, and reaction mechanisms

• Demonstrated experience in spectroscopy simulations, including XANES, EXAFS, and Mössbauer spectroscopy

• Proficiency in Python and relevant computational platforms

• At least 1–2 years of experience adapting and implementing AI/ML methods in catalysis, including:

  • machine learning interatomic potentials

  • agentic workflows

• Experience in proposal writing and funding applications

• Experience mentoring postdoctoral researchers and graduate students

• Excellent written and oral communication skills

• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork

Preferred Qualifications

• Experience with PGM-free oxygen reduction reaction (ORR) catalysts

• Experience with CO₂ reduction catalysis

RD2: Bachelors and 5+ years of experience, Masters and 3+ years, or PhD and 0+ years, or equivalent

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Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

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As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.