What the work is
Key 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.
- 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.
Who gets hired
- 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 Qualifications
- 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.
Engagement Details
- Commitment: Minimum 10 hours per week, with potential for up to 40 hours per week.
The platform posted this on 11 Oct 2026.