[Hiring] Atomistic Materials Modeling Expert @Mercor

Role Description

Evaluate advanced scientific problems involving atomistic simulations and computational materials science.

• Review and validate AI-generated answers involving DFT calculations, molecular dynamics, and materials property predictions.
• Assess the appropriateness of computational methods, simulation parameters, convergence criteria, and physical assumptions.
• Analyze atomic structures, electronic properties, reaction energetics, diffusion mechanisms, and materials stability.
• Develop high-quality reference solutions, evaluation rubrics, and technically rigorous scientific explanations.
• Apply computational expertise to improve AI performance on complex materials science problems.

Qualifications

• Must-Have:
  • PhD in Computational Materials Science, Materials Science and Engineering, Computational Chemistry, Condensed Matter Physics, Chemical Engineering, or a closely related field. Exceptional MSc candidates with substantial relevant experience may be considered.
  • Demonstrated hands-on experience with at least one of the following:
    • Density Functional Theory (DFT)
    • Ab initio Molecular Dynamics (AIMD)
    • Classical Molecular Dynamics (MD)
    • Monte Carlo simulations
    • Atomistic or first-principles materials modelling
  • Proficiency with one or more computational tools, such as VASP, Quantum ESPRESSO, CP2K, GPAW, LAMMPS, or similar software.
  • Strong understanding of electronic structure, interatomic interactions, thermodynamics, statistical mechanics, and materials properties.
  • Experience interpreting simulation results and validating computational predictions.
  • Strong Python skills for scientific computing, data analysis, or workflow automation.
• Preferred:
  • Experience with ASE, pymatgen, or computational materials workflow tools.
  • Expertise in surface and interface modelling, heterogeneous catalysis, semiconductor materials, or reaction energetics.
  • Experience with high-throughput computational materials screening.
  • Familiarity with machine-learned interatomic potentials and AI-assisted materials discovery.
  • Peer-reviewed publications demonstrating computational materials research.
  • Experience integrating experimental materials characterisation with atomistic simulations.

Application Process

• Upload resume
• AI interview based on your resume
• Submit form

Resources & Support

• For details about the interview process and platform information, please check: Interview Process
• For any help or support, reach out to: [email protected]
• PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.