2027 Summer Intern - Research and Development - Aftertreatment Catalysis

GM•Warren, MI

6d•Hybrid

About The Position

General Motors is seeking a motivated student to join the Energy and Propulsion Systems Research team as a Summer Intern in aftertreatment catalysis. The intern will contribute to research supporting next-generation automotive emissions-control technologies, including catalyst performance, durability, materials development, reaction kinetics, system modeling, and data-driven materials discovery. The specific project will be aligned with the candidate’s skills and the team’s research priorities, and may suit students with experimental, computational, modeling, data science, or artificial intelligence and machine learning backgrounds. The team is re-establishing and advancing GM’s aftertreatment catalysis research capabilities. Its research areas include three-way catalysts, platinum-group-metal reduction, catalyst aging and durability, alternative catalyst and substrate materials, hydrocarbon and nitrogen-oxide traps, reaction kinetics, predictive modeling, and AI/ML-enabled materials discovery. Depending on their background, the intern may design and analyze laboratory experiments; prepare samples and support testing; investigate catalyst performance and degradation; use characterization methods; develop models; process data; apply statistical learning, machine learning, optimization, or uncertainty quantification; build data-driven screening tools; explore AI/ML for materials discovery; correlate material properties and performance; document and present findings; and collaborate with internal and external research partners. The intern must follow laboratory safety procedures, data-quality practices, and documentation requirements. This is a hybrid internship based in Warren, Michigan. The successful candidate is expected to report onsite at least three times per week, or at another frequency dictated by the business. Internship start dates are May 24, 2027, and June 14, 2027. The role is subject to export-control restrictions and requires the successful candidate to be a U.S. Person. GM does not provide immigration-related sponsorship for this role.

Requirements

Currently pursuing a master’s or doctoral degree in chemistry, chemical engineering, materials science, materials engineering, mechanical engineering, computer science, data science, physics, or a related STEM field.
Available to work full-time, 40 hours per week during the summer internship.
Demonstrated interest in at least one of the following: heterogeneous catalysis, reaction engineering, emissions control, or materials science; laboratory experimentation and analytical characterization; mathematical modeling, simulation, scientific computing, or numerical methods; or data science, artificial intelligence, machine learning, or optimization.
Ability to work independently and collaboratively in a multidisciplinary research environment.
Strong analytical, problem-solving, communication, and technical documentation skills.
Must be a U.S. Person (U.S. citizen, U.S. permanent resident, asylee, or refugee) because the position is subject to export-control restrictions.
Must not require GM immigration-related sponsorship now or in the future.

Responsibilities

Design, conduct, and analyze laboratory experiments for catalyst, washcoat, powder, or aftertreatment core evaluation.
Prepare samples and support testing using laboratory reactor systems and analytical tools.
Investigate catalyst activity, selectivity, conversion efficiency, thermal durability, aging, sintering, and degradation mechanisms.
Apply characterization methods such as Fourier-transform infrared spectroscopy, diffuse-reflectance infrared Fourier-transform spectroscopy, surface-area analysis, chemisorption, microscopy, spectroscopy, or related techniques.
Develop reaction-kinetic, catalyst-performance, degradation, or system-level models.
Process and interpret experimental data using Python, MATLAB, or similar tools.
Apply statistical learning, machine learning, optimization, or uncertainty quantification to catalyst and materials datasets.
Build data-driven tools to accelerate catalyst screening and reduce reliance on hardware testing.
Explore AI/ML approaches for materials discovery, composition optimization, performance prediction, or experiment planning.
Correlate material properties, operating conditions, and catalyst performance to identify design principles.
Document methods, analyze results, prepare technical summaries, and present findings to research and engineering teams.
Collaborate with researchers, engineers, universities, national laboratories, suppliers, and other GM organizations.
Follow laboratory safety procedures, data-quality practices, and applicable documentation requirements.