Entry-Level - Researcher, Aftertreatment Catalysis

General Motors Company; Warren (MI); On-site; USD 90,000 - 120,000; Full time

Job summary

General Motors is seeking an early-career Researcher to advance aftertreatment catalysis technologies, combining fundamental science with hands-on experimentation and AI/ML to develop robust catalyst designs for automotive applications.

You will design and execute lab studies, interpret catalyst data, and collaborate with modeling and engineering teams to translate findings into product-relevant recommendations, mentor junior colleagues, and contribute to focused external and internal programs.

Qualifications

• Pursuit/completion of a PhD in Chemistry, Materials Science, chemical/Materials Engineering, or related field.
• Graduating Aug 2026–Jun 2027.
• Able to work fulltime, 40 hours per week.

Responsibilities

• Conduct research on automotive aftertreatment catalysts, emphasis on three-way catalysts, performance, durability, cost optimization.
• Design, plan, execute, and document catalyst evaluation studies using laboratory reactor systems.
• Develop catalyst structures, formulations, and architectures to reduce Pt group metal content while maintaining performance.
• Apply catalyst characterization methods (FTIR-DRIFTS, porosimetry, BET, chemisorption, TEM) to understand active sites and interactions.
• Correlate catalyst composition and surface chemistry to reactor performance to identify design rules.
• Build and maintain high-quality datasets from experiments, literature, simulations, and characterization results for modeling.
• Apply AI/ML methods to guide catalyst design and experimental planning.
• Use interpretable models to identify catalyst-performance relationships and robust designs.
• Close the design-build-test-learn loop by validating AI/ML hypotheses and refining models.
• Collaborate with modeling/simulation teams to develop predictive tools for catalyst design decisions.
• Engage with universities, labs, suppliers, and internal teams on focused research programs and publications.

Skills

Heterogeneous catalysis

Laboratory experimentation

Data analysis

Python/MATLAB/R

AI/ML basics

Scientific communication

Education

PhD in Chemistry/Materials Science/Chemical/Materials Engineering

Tools

FTIR-DRIFTS

porosimetry

BET surface area

Chemisorption

X-ray methods

Electron microscopy

Job description

Job Description

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:

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.

What You'll Do:

• 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

Required Qualifications:

• 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

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 emissi