Atomistic Modelling Expert - AI Research - AI Trainer

Mercor is recruiting Atomistic Materials Modelling Experts with strong backgrounds in computational materials science, quantum mechanical simulations, and molecular modelling to support advanced AI research. Experts will evaluate technically demanding problems involving atomistic simulations, materials properties, computational methodologies, and scientific reasoning. The ideal candidate has extensive hands-on experience with density functional theory (DFT), molecular dynamics (MD), and other atomistic modelling techniques, alongside a strong understanding of the underlying physical principles.

• 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.

• Analyse atomic structures, electronic properties, reaction energetics, diffusion mechanisms, and materials stability.

• Evaluate surface and interface phenomena, adsorption energies, defect formation, and chemical reaction pathways.

• Develop high-quality reference solutions, evaluation rubrics, and technically rigorous scientific explanations.

• Identify computational inaccuracies, methodological limitations, and errors in scientific reasoning.

• Apply computational expertise to improve AI performance on complex materials science problems.

• 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.

• 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.

• Commitment:
  Minimum 10 hours per week, with potential for up to 40 hours per week.

• Apply advanced computational expertise to frontier AI research.

• Help develop AI systems capable of understanding and solving sophisticated materials science problems.

• Work on challenging scientific evaluations involving quantum mechanics, atomistic simulations, and materials discovery.

• Collaborate with teams working at the intersection of AI and scientific research.