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Stellenbeschreibung

Atomistic Modelling Expert - AI Research in Berlin, Deutschland

Mercor

  • RemoteArbeiten von überall
  • VertragBefristet oder freiberuflich

84 $ pro Stunde

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

  • For details about the interview process and platform information, please check:
  • 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.

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