Computational Materials Scientist - PhD

Mercor
San Francisco, CA
Category Research
Remote
Job Description
Role Overview

Contribute domain expertise in first-principles and molecular simulation to build high-quality training and evaluation data. Review and evaluate AI-generated scientific reasoning to improve technical accuracy. Design and solve expert-level problems in atomistic and surface modeling.

What You Will Do

Deliver reliable, high-quality work within defined timelines. Rate and rank model outputs against scientific criteria with clear reasoning. Structure technical knowledge into well-organized, model-ready data.

Why It Might Be a Fit

Must have hands-on experience with atomistic modeling using first-principles or molecular methods. Proficiency with standard tooling (e.g., VASP, Quantum ESPRESSO, CP2K, GPAW, LAMMPS, ASE, pymatgen). PhD in materials science, chemistry, physics, chemical engineering, or a related field.

Requirements

  • Hands-on experience with atomistic modeling using first-principles or molecular methods (DFT, ab initio molecular dynamics, classical MD, or Monte Carlo)
  • Experience modeling surfaces, interfaces, and adsorption or reaction phenomena
  • Proficiency with standard tooling (e.g., VASP, Quantum ESPRESSO, CP2K, GPAW, LAMMPS, ASE, pymatgen)
  • PhD in materials science, chemistry, physics, chemical engineering, or a related field
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