Atomistic Modelling Expert - AI Research in Berlin, Deutschland

About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark , General Catalyst , Peter Thiel , Adam D'Angelo , Larry Summers , and Jack Dorsey .

Position: Atomistic Materials Modeling Expert

Type: Contract

Compensation: $84/hour

Location: Remote

Commitment: 10–40 hours/week

Role Responsibilities

• 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 (Takes 20–30 mins to complete)

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

Resources & Support

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• 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.