Computational Materials Scientist (Atomistic Simulation)

About NexstromNexstrom is a Singapore-based deep-tech company building the materials foundation for the next era of semiconductors. Silicon is approaching its physical limits, and the industry's path forward runs through atomically thin 2D semiconductors — materials that until now have lived in research labs, not fabs.Nexstrom is closing that gap: we grow single-crystal 2D semiconductor wafers on production-ready tools that we built, turning demonstrated lab results into manufacturable technology. We're a small team of scientists and engineers working at the frontier of materials science, and we're looking for people who want to help take a new class of semiconductors from lab to fab.About the RoleWe are looking for a Computational Materials Scientist to join our team and support advanced semiconductor materials and process development through first-principles calculations, atomistic simulations and scientific computing.This role will focus on understanding the fundamental mechanisms governing material growth and surface chemistry, including adsorption, diffusion, reaction pathways, interface interactions and finite-temperature material behaviour.You will work closely with our experimental teams and external computational-platform partners to develop physically meaningful models, investigate reaction mechanisms and translate simulation results into actionable insights for materials and process development.We are looking for someone who combines strong physical intuition with hands-on computational expertise, and who is comfortable independently developing, programming and automating simulation workflows.Key ResponsibilitiesPerform first-principles and atomistic simulations, including Density Functional Theory (DFT), Nudged Elastic Band (NEB/CI-NEB), Ab Initio Molecular Dynamics (AIMD) and classical molecular dynamics.Develop atomistic models of surfaces, interfaces, defects, adsorbates and reaction intermediates relevant to semiconductor materials and thin-film growth.Investigate adsorption mechanisms, surface diffusion, reaction energetics, activation barriers and competing reaction pathways.Develop and automate Python-based simulation workflows, including structure generation, job submission, data extraction, analysis and visualisation.Integrate computational workflows with external scientific computing platforms through APIs, SDKs, command-line tools and scripting.Apply advanced computational techniques, including machine-learning interatomic potentials, enhanced sampling and kinetic Monte Carlo methods, where appropriate.Translate experimental observations into computational models and testable predictions to support materials and process optimisation.Collaborate closely with experimental researchers to interpret simulation results, validate hypotheses and refine computational strategies.Ideal ProfilePhD in Computational Materials Science, Computational Chemistry, Materials Science, Physics, Chemical Engineering or a related discipline.Strong hands-on experience with DFT calculations using software such as VASP, Quantum ESPRESSO, QuantumATK, CASTEP or equivalent.Demonstrated experience modelling surfaces, interfaces, adsorption, diffusion, defects or chemical reactions.Practical experience with NEB/CI-NEB, transition-state calculations or activation-barrier determination.Experience with molecular dynamics simulations, preferably including AIMD.Strong Python programming skills, particularly for scientific computing, data analysis and workflow automation.Proficiency working in Linux and high-performance computing (HPC) environments, including scripting, job schedulers and version control.Ability to independently develop, troubleshoot and automate computational workflows.Experience in one or more of the following areas would be advantageous:Computational surface chemistry or heterogeneous catalysis.Chemical Vapour Deposition (CVD), Atomic Layer Deposition (ALD) or thin-film reaction modelling.Semiconductor materials and process simulation.Oxide surfaces and gas–surface reactions.Scientific computing frameworks such as ASE, pymatgen, atomate/jobflow or LAMMPS.Machine-learning interatomic potentials.Kinetic Monte Carlo or enhanced-sampling methods.API/SDK-based integration with scientific computing platforms.Why Join Nexstrom“To change what’s possible, we have to question the limits we’ve accepted.” — Dr. Lance Li, Co-FounderNobody has solved this yet. Growing 2D materials on 12-inch wafers at yields a fab will accept is an open problem the whole industry needs answered — and we are close.Real hardware, real wafers, real partners. We shipped commercial wafers in 2026. You will not be building slideware.Semiconductor industry depth. Our team and advisors bring experience from TSMC, imec and Applied Materials. In a team our size, you work with them directly, not through three layers.Well-backed. Nexstrom is backed by Xora Innovation, SEEDS Capital and Foothill Ventures, with US$15M raised.Competitive package with AWS, performance bonus, medical and insurance coverage.