General Motors
Entry-Level - Researcher — Computational Materials Science: Continuum Scales and Materials Discovery
Warren, Michigan, United States of America · Hybrid · Entry level · Full-time · Posted Oct 12, 2026
- Experience
- No specific number of years stated.
- Education
- Advanced degree (M.S. or Ph.D.) in Materials Science and Engineering, Mechanical Engineering, Computational Science, Applied Mathematics, Physics, Chemistry, or a closely related field.
- Visa sponsorship
- GM does not provide immigration-related sponsorship now or in the future; the successful candidate must be a U.S. Person (U.S. citizen, U.S. permanent resident, asylee, or refugee).
About the job
Develops continuum-scale computational models and data-driven workflows to discover and evaluate materials for vehicle and manufacturing applications, in collaboration with experimental and engineering teams.
What you'll do
- Plan and conduct collaborative computational materials research, including developing and applying models and simulation workflows.
- Use computation, optimization, and data-driven methods to screen materials and guide experiments and materials down-selection.
- Communicate research through reports, publications, presentations, software documentation, and invention disclosures.
What they ask for
- Advanced degree (M.S. or Ph.D.) in a listed or closely related field; must be graduating between August 2026 - June 2027.
- Able to work fulltime, 40 hours per week.
- Strong foundation in materials thermodynamics, kinetics, mechanics, transport, numerical methods, and structure–property relationships; experience developing or applying continuum-scale models.
- Proficiency in scientific programming and data analysis, plus experience with numerical algorithms, high-performance computing, version-controlled software, and reproducible computational workflows.
Nice to have
- Ph.D. strongly preferred.
- Experience in materials discovery, ICME, scientific machine learning, computational thermodynamics, scalable simulation, experimental validation, or translating computational insights into engineering or manufacturing solutions.
Tools
Python, C/C++, Fortran, MATLAB, Julia, finite-element analysis, phase-field modeling, crystal plasticity
AI focus
Uses data-driven methods, optimization, high-throughput computation, and potentially scientific machine learning to screen and discover materials.
Model-written summary · the original posting is authoritative