Researcher, Aftertreatment Catalysis

About the Role

The Researcher will contribute to aftertreatment catalysis research, combining strong scientific fundamentals with practical understanding of automotive emissions systems. This individual will design and execute laboratory studies, interpret catalyst characterization data, and help translate research findings into product-relevant recommendations for catalyst formulation, structure, and system integration. The role also includes collaboration with engineering, modeling, and external research teams, with opportunities to grow technical depth through mentorship.

The Team

General Motors is seeking an early-career Researcher to advance aftertreatment catalysis technologies that improve emissions performance, durability, and cost competitiveness for automotive applications. This role is well suited for a researcher or recent PhD graduate who is excited to combine fundamental catalytic science, hands-on experimentation, data analytics, and artificial intelligence/machine learning (AI/ML) to develop robust catalyst designs.

Key Responsibilities

• Conduct research on automotive aftertreatment catalysts, with emphasis on three-way catalyst technologies and their performance, durability, and cost optimization.
• Design, plan, execute, and document catalyst evaluation studies using laboratory reactor systems and related experimental methods.
• Develop and assess catalyst structures, formulations, and architectures intended to reduce platinum group metal content while maintaining or improving conversion efficiency and durability.
• Apply catalyst characterization methods—including FTIR-DRIFTS, porosimetry, BET surface area analysis, chemisorption, transmission electron microscopy, and related techniques—to understand active sites, support interactions, dispersion, and aging.
• Correlate catalyst composition, structure, surface chemistry, and reactor performance to identify design rules for next-generation aftertreatment materials.
• Build and maintain high-quality datasets from experiments, literature, simulations, and characterization results, including test conditions, metadata, and data-quality information needed for reproducible modeling.
• Apply AI/ML methods such as feature engineering, regression, classification, clustering, dimensionality reduction, Bayesian optimization, active learning, and uncertainty quantification to guide catalyst design and experimental planning.
• Use interpretable and physically informed models to identify catalyst-performance relationships and prioritize designs that are robust across operating conditions, aging histories, and material variability.
• Close the design-build-test-learn loop by experimentally validating AI/ML-generated catalyst hypotheses and using the results to improve models and select the next experiments.
• Partner with modeling and simulation teams to develop predictive tools that reduce dependence on hardware-based testing and support catalyst and converter design decisions.
• Collaborate with universities, national laboratories, suppliers, and internal stakeholders on focused research activities, externally sponsored technical programs, technical reports, research summaries, invention disclosures, presentations, and publications, as appropriate.

Requirements & Tech Stack

• PhD degree in one of the following areas: Chemistry, Materials Science, Chemical Engineering, Materials Engineering, or a closely related field.
• Must be graduating between August 2026 - June 2027.
• Able to work fulltime, 40 hours per week.
• Research experience in heterogeneous catalysis, reaction engineering, materials chemistry, surface science, or a closely related area, with interest in automotive aftertreatment applications.
• Experience designing and conducting laboratory experiments, analyzing data, and drawing scientifically defensible conclusions.
• Working knowledge of catalyst characterization techniques and the ability to interpret results from methods such as FTIR-DRIFTS, surface area and porosimetry, chemisorption, X-ray methods, electron microscopy, or related tools.
• Demonstrated ability to connect catalyst composition or structure with reaction performance, selectivity, stability, or degradation mechanisms.
• Foundational experience with scientific programming and data analysis, preferably using Python, MATLAB, R, or a similar environment.
• Ability to learn and apply AI/ML methods to scientific problems, including data preprocessing, feature selection, model evaluation, and uncertainty-aware prediction.
• Strong written and verbal communication skills, with the ability to work effectively in multidisciplinary research and engineering teams.

Preferred Qualifications

• Research experience in three-way catalysts, automotive emissions control, oxygen storage materials, catalyst aging, sulfur or thermal durability, precious-metal utilization, or catalyst-support interactions.
• Experience evaluating catalysts in flow reactors, bench-scale emissions systems, or other controlled reaction environments.
• Coursework, research, or project experience in machine learning, design of experiments, Bayesian optimization, active learning, digital experimentation, or scientific informatics.
• Familiarity with kinetic modeling, reaction mechanism development, multiscale modeling, or physics-informed and data-driven modeling approaches.
• Experience working with high-dimensional, time-series, or multimodal scientific data and communicating model limitations and uncertainty.
• Publications, patents, conference presentations, or other evidence of research contributions in catalysis, materials, emissions control, AI/ML for science, or related fields.

Note: This role is subject to export control restrictions and requires the successful candidate to be a U.S. Person (U.S. citizen, U.S. permanent resident, asylee or refugee).

Work Arrangement: Hybrid. Expected to report to Warren, MI three times per week, at minimum.