Entry-Level – Researcher, Aftertreatment Catalysis

General Motors•Warren, MI

4d•Hybrid

About The Position

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. At General Motors, we are 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.

Requirements

Pursuit/Completion of a 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; formal ML specialization is not required
Strong written and verbal communication skills, with the ability to work effectively in multidisciplinary research and engineering teams

Nice To Haves

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

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