2027 Summer Intern - Research and Development - Aftertreatment Catalysis at General Motors in Warren, MI

Job description

Job Description The Role 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. This opportunity is intentionally broad and may be well suited for students with experimental, computational, modeling, data science, or artificial intelligence and machine learning backgrounds. The specific project will be aligned with the candidate’s skills and the team’s research priorities. The Team The team is re-establishing and advancing GM’s aftertreatment catalysis research capabilities. Current 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. What You’ll Do Depending on your background, you may: 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 all laboratory safety procedures, data-quality practices, and applicable documentation requirements. Required Qualifications 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. 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. GM does not provide immigration-related sponsorship for this role. Do not apply for this role if you will need GM immigration sponsorship now or in the future. This includes direct company sponsorship, entry of GM as the immigration employer of record on a government form, and any work authorization requiring a written submission or other immigration support from the company (e.g., H1-B, OPT, STEM OPT, CPT, TN, J-1, etc.) The position is subjec