Remote | Computational Materials Scientist

We are sharing a specialised part-time consulting opportunity for experienced computational materials scientists with deep expertise in atomistic modelling, surface and interface science, first-principles simulation, and computational catalysis.

This long-term role supports an advanced AI research initiative focused on materials science and the physical sciences. Selected professionals will apply expert-level knowledge of atomistic simulation, electronic structure, surfaces, adsorption, and reaction energetics to develop scientific training data, evaluate AI-generated reasoning, and create technically rigorous problems and reference solutions.

• Apply first-principles and molecular simulation methods to complex materials-science problems
• Work with approaches including DFT, ab initio molecular dynamics, classical molecular dynamics, and Monte Carlo methods
• Develop technically accurate simulation setups and reference analyses
• Evaluate assumptions, boundary conditions, convergence choices, and modelling methodology
• Apply professional scientific judgment to realistic computational materials scenarios

• Develop and evaluate models involving surfaces, interfaces, adsorption, and reaction phenomena
• Work with slab models, surface reconstructions, and adsorption configurations
• Analyse reaction pathways, transition states, and activation energetics
• Apply NEB and related methods where appropriate
• Evaluate microkinetic and surface-reaction models within relevant scientific contexts

• Design and solve challenging expert-level problems in atomistic and surface modelling
• Review AI-generated scientific reasoning for technical accuracy and completeness
• Identify errors involving simulation methodology, energetics, structure, or physical interpretation
• Rate and rank model outputs against defined scientific criteria
• Provide concise written reasoning supporting evaluation decisions

• Structure simulation methods, parameters, workflows, and results into organised model-ready data
• Develop high-quality reference material for scientific training and evaluation
• Ensure technical information is internally consistent and reproducible
• Translate specialised computational knowledge into clear written explanations
• Deliver reliable work according to defined project timelines and quality standards

Strong candidates may have:

• Hands-on expertise in atomistic modelling using first-principles or molecular simulation methods
• Experience with DFT, ab initio molecular dynamics, classical MD, Monte Carlo, or related techniques
• Substantial experience modelling surfaces, interfaces, adsorption, or chemical reactions
• Familiarity with slab models, surface reconstructions, transition-state analysis, NEB, or microkinetics
• Experience with semiconductor-relevant materials or computational heterogeneous catalysis
• Strong scientific reasoning and quantitative problem-solving skills
• Ability to explain complex computational methodology clearly and concisely
• Availability for at least 10 hours per week
• Current residence in the United States

• A PhD in materials science, chemistry, physics, chemical engineering, or a closely related field is expected
• Several years of research experience beyond the PhD may strengthen an application
• Strong research experience in computational materials science, surface science, or catalysis is particularly valuable

• Experience with VASP
• Familiarity with Quantum ESPRESSO, CP2K, or GPAW
• Experience with LAMMPS
• Proficiency with ASE, pymatgen, or related computational materials tools
• Background in semiconductor materials modelling
• Experience in computational…