Lead Modeling Scientist
On-siteKennesaw, Georgia, United States
Job Summary
Develop and maintain Integrated Computational Materials Engineering (ICME) models linking process parameters, microstructure, and properties for flat aluminum sheet products across casting, rolling, and heat treatment. Design multi-scale and constitutive behavior models to predict flow stress, formability, and failure, while automating workflows for microstructure-property relationships. Integrate simulation results with experimental characterization and plant data to validate predictions and improve model accuracy. Lead end-to-end modeling projects from scoping through deployment, collaborating with plant engineers to troubleshoot issues and optimize productivity.
Required Qualifications
- Advanced degree (M.S. or Ph.D.) in Materials Science, Mechanical Engineering, or a related field
- More than ten years of experience in computational modeling
- Expertise in metallurgy and materials science
- Process and microstructure modeling for metallic systems, especially aluminum alloys
- Flat rolled aluminum products experience
- Proficiency in modeling software such as Thermo-Calc, DICTRA, TC-Prisma, and Pandat
- Proficiency in numerical methods including finite element, finite difference, cellular automata, and phase-field modeling
- Programming skills in MATLAB, FORTRAN, and C/C++
- Proficiency in statistical analysis tools like JMP and R
- Strong understanding of metallurgical principles and characterization techniques
- Excellent technical communication and project management skills
- Proven track record of solving plant issues through modeling with measurable business impact
- Experience deploying predictive models in production environments to improve throughput and quality
- Legally authorized to work in the United States without the need for current or future sponsorship
Desired Qualifications
- Machine Learning for materials: Property prediction, Process window optimization, Defect classification
- Digital twin development: End-to-end process-to-property simulation
- High-performance computing (HPC): Parallel simulations, cloud computing
- Data pipelines & model deployment: MLOps, version control, model governance
- DOE (Design of Experiments)
- Multi-variable regression & sensitivity analysis
- Model calibration & uncertainty quantification
Additional Requirements
- Unable to provide visa sponsorship; candidates must be legally authorized to work in the United States without the need for current or future sponsorship
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