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Materials Science Expert - Remote

YO IT CONSULTINGRemote

Posted September 9, 2026

Job Description

Materials Science Expert

Role Type: Contractor
Location: Global, Fully Remote
Schedule: ~15 hours/week, flexible

About the Role

We’re seeking a Materials Science Expert to support an AI training project involving computational materials science, materials modeling, scientific simulation, and Python. You’ll solve, validate, and review materials-engineering tasks and create reproducible, programmatic solutions. No prior AI experience is required.

Key Responsibilities

  • Solve and validate computational materials science problems.

  • Build material structures, compositions, and simulation inputs.

  • Run and analyze atomistic, electronic, molecular-dynamics, continuum, or electrochemical simulations.

  • Use Python to automate simulations, parameter sweeps, and data analysis.

  • Analyze mechanical, thermal, electrical, chemical, and structural properties.

  • Diagnose convergence, numerical, modeling, and physical-assumption issues.

  • Compare results with experiments, literature, and expected physical trends.

  • Review AI-generated solutions and develop reliable reference solutions.

Required Skills

  • Materials Science & Engineering

  • Computational Materials Modeling

  • Materials Simulation & Analysis

  • Python

  • Scientific Computing

  • Structure–Property Relationships

  • Numerical Validation & Troubleshooting

  • Technical Problem-Solving

Tools

Experience with tools such as LAMMPS, ASE, pymatgen, Quantum ESPRESSO, FEniCSx, CalculiX, Elmer, PyBaMM, or equivalent CLI/API-based software. Familiarity with NumPy, SciPy, pandas, Matplotlib, or Jupyter is beneficial.

Qualifications

  • MS/PhD in Materials Science, Metallurgy, or related field; or Mechanical/Chemical Engineering with strong materials specialization.

  • Experience in computational materials modeling, simulation, characterization, or materials R&D.

  • Strong Python and programmatic engineering-tool experience.

  • Ability to explain modeling assumptions, validate results, and distinguish computational errors from physical behavior.