2027 Summer Intern - Research and Development - Computational Materials Science: Continuum Scales and Materials Discovery

Location: Warren, Michigan, United States of America

Job Description

The Role

As a valued Intern in the Mechanics, Materials, and Process Research Lab at General Motors Research and Development, you will advance computational materials science at continuum length scales to accelerate the discovery, design, and deployment of materials for next-generation vehicle and manufacturing applications. You will propose new research directions, establish technical research plans, and lead collaborative studies that connect material composition, microstructure, processing, performance, and engineering requirements.

You will develop and apply continuum-scale models, simulation workflows, and data-driven methods to generate new insights into materials behavior across relevant spatial and temporal scales. Your work will guide experimental research, enable efficient materials down-selection, and help translate computational discoveries into practical materials and manufacturing solutions. You will document and communicate results through high-quality technical reports, external publications, presentations, software or workflow documentation, and records of invention.

The Team

This position supports the development of advanced materials and manufacturing technologies for future GM products and processes. The intern will work at the intersection of continuum-scale simulation, materials discovery, experimental validation, and application-focused engineering.

What You’ll Do

Plan and execute collaborative research in computational materials science, with emphasis on continuum-scale materials behavior and materials discovery.

Develop physics-based models for composition–process–structure–property relationships, including constitutive behavior, phase evolution, transport, damage, fracture, fatigue, thermal response, and multiphysics phenomena as appropriate.

Build and apply continuum and mesoscale simulation methods such as finite-element, phase-field, crystal-plasticity, computational fluid dynamics, reaction–diffusion, cellular automata, or related approaches.

Integrate thermodynamics, kinetics, mechanics, processing science, and materials data into predictive computational workflows.

Use simulation, high-throughput computation, optimization, and data-driven methods to screen candidate materials, compositions, processing conditions, and architectures.

Partner closely with experimental researchers to define informative experiments, interpret results, quantify uncertainty, and accelerate materials down-selection.

Establish model verification, validation, calibration, uncertainty quantification, and sensitivity-analysis practices appropriate for research and engineering use.

Connect computational materials insights to manufacturing processes and vehicle-relevant performance requirements.

Drive internal and external collaborations with GM engineering teams, universities, national laboratories, software developers, and research partners.

Maintain awareness of the state of the art in continuum mechanics, computational materials science, materials informatics, scientific machine learning, and related disciplines.

Prepare timely, high-quality technical reports, presentations, publications, software documentation, and invention disclosures.

Generate intellectual property to protect new inventions, computational methods, and research results.

Assist and mentor other employees in accomplishing project and research goals.

Required Qualifications

Pursuit of an Advanced degree (M.S. or Ph.D.) in one of the following areas: Materials Science and Engineering, Mechanical Engineering, Computational Science, Applied Mathematics, Physics, Chemistry, or a closely related field.

Must be a current student

Strong foundation in materials thermodynamics, kinetics, mechanics, transport, numerical methods, and structure–property relationships.

Experience developing or applying continuum-scale models to materials, manufacturing processes, or engineering systems.

Experience with one or more of the following: finite-element analysis, phase-field modeling, crystal plasticity, constitutive modeling, damage and fracture, multiphysics simulation, reaction–diffusion, or process simulation.

Experience connecting material chemistry, microstructure, processing history, and performance through computational models.

Proficiency in scientific programming and data analysis using tools such as Python, C/C++, Fortran, MATLAB, Julia, or similar languages.

Experience with numerical algorithms, high-performance computing, version-controlled software, and reproducible computational workflows.

Ability to formulate research questions, translate physical assumptions into models, and communicate model limitations clearly.

Experience working in multidisciplinary research teams.

Strong written and verbal communication skills to work effectively with GM internal and external collaborators.

Able to work fulltime, 40 hours per week during the summer months

Preferred Qualifications

Ph.D. strongly preferred.

Experience with one or more of the following

Integrated Computational Materials Engineering (ICME) and materials process–structure–property modeling.

Materials discovery, high-throughput screening, combinatorial design, optimization, Bayesian methods, or active learning.

Phase-field, crystal-plasticity, mesoscale, finite-element, multiphysics, or reduced-order modeling.

Computational thermodynamics, CALPHAD, kinetics, phase transformation, precipitation, solidification, or microstructure evolution.

Scientific machine learning, surrogate modeling, uncertainty quantification, sensitivity analysis, or inverse problems.

Digital twins, process–structure–performance linkages, or integration of simulation with manufacturing data.

Parallel computing, GPU acceleration, cloud or cluster computing, and scalable simulation workflows.

Scientific software development, open-source contributions, workflow automation, or user-defi