Computational Materials Science Specialist
About the Role
A high-impact AI training initiative focused on computational materials science, materials modeling, scientific simulation, and Python. The work centers on producing and validating reproducible solutions to technical materials-engineering problems.
This opportunity is ideal for individuals with advanced materials expertise and practical experience using computational or scientific tools programmatically. Strong candidates should be able to connect material structure, composition, processing, properties, and performance while evaluating both computational and physical validity.
The work involves constructing material and atomic models, running simulations, analyzing engineering properties, diagnosing numerical or modeling issues, and reviewing AI-generated technical solutions. Reproducibility, sound physical reasoning, and clear technical validation are critical.
What You'll Do
- Create material structures, atomic configurations, compositions, and solver-ready inputs
- Run and validate atomistic, electronic-structure, molecular-dynamics, continuum, electrochemical, or related simulations
- Use Python to automate calculations, generate inputs, process results, and conduct parameter sweeps
- Analyze mechanical, thermal, electrical, chemical, structural, and electrochemical properties
- Model relationships between composition, structure, processing, properties, and performance
- Diagnose failed calculations, convergence issues, numerical instability, invalid structures, and incorrect physical assumptions
- Compare computational results against experimental data, literature values, and established physical trends
- Review AI-generated solutions for scientific accuracy and identify invalid assumptions or conclusions
- Develop reproducible reference solutions and objective verification procedures
Requirements
- MS or PhD in Materials Science and Engineering, Metallurgy, or a closely related discipline
- MS or PhD in Mechanical or Chemical Engineering with substantial materials specialization is also acceptable
- Strong understanding of materials behavior and structure-property relationships
- Practical experience in computational materials modeling, simulation, characterization, or materials-focused engineering analysis
- Proficiency with Python
- Experience using at least one CLI-accessible, scriptable, configuration-based, or API-driven engineering/scientific tool
- Ability to justify modeling assumptions, parameters, approximations, and convergence criteria
- Ability to distinguish computational failures from genuine physical behavior
- Ability to communicate complex scientific reasoning and technical limitations clearly
- Experience with tools such as LAMMPS, ASE, pymatgen, Quantum ESPRESSO, FEniCSx, CalculiX, Elmer, PyBaMM, or equivalent is preferred
- Experience with NumPy, SciPy, pandas, Matplotlib, Jupyter, materials-informatics libraries, or comparable scientific Python tooling is preferred
- Academic research, national laboratory, industry R&D, or computational engineering experience is preferred
- Availability to contribute approximately 15 hours per week on a flexible schedule
- Ability to work fully remotely and begin work promptly following onboarding