layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

LAYIQ - Entry-Level - Researcher — Computational Materials Science: Continuum Scales and Materials Discovery at General Motors

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: 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.
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 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.

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-defined solver development.
Experience coupling continuum-scale predictions with experimental validation and characterization data.
Demonstrated publication record and/or intellectual property generation.
Experience transitioning computational materials insights into applied engineering or manufacturing solutions.
Experience communicating technical results to both specialist research audiences and product-development or manufacturing teams.
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 subject to export control restrictions and requires the successful candidate to be a U.S. Person (U.S. citizen, U.S. permanent resident, asylee or refugee).
Work Arrangement: Hybrid: This role is categorized as hybrid. This means the successful candidate is expected to report to Warren, MI three times per week, at minimum [or other frequency dictated by the business].
To help facilitate administration of relocation benefits if you are selected, please apply using the permanent address you would move from.




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Entry-Level - Researcher — Computational Materials Science: Continuum Scales and Materials Discovery

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