Position Summary
As a Computational Materials Discovery Scientist, you will work at the intersection of materials science, computational chemistry, condensed matter physics, and quantum computing. You will contribute to molecular and mesoscale modeling for polymeric material. This role is ideal for candidates who want to solve real scientific and industrial problems using multiscale modeling.
Requirements
Core Technical Skills
Molecular & Statistical Simulations
Classical Molecular Dynamics (MD)Force-field development and validationMonte Carlo (MC) simulationsMultiscale & Mesoscale Modeling
Coarse-grained modeling for highly heterogeneous systemsPhase-field modelingBenchmarking & Validation
MD engines: LAMMPS, GROMACS, NAMDTools
MDAnalysis, pymatgen, PLUMED, VOTCA, PACKMOLSimulation Workflow Engineering
Build reproducible, automated workflows in Python for:High-throughput materials screening
MD-CG-Mesoscale simulation pipelines
Data extraction & post-processing
Parameters generations
HPC clusters
Cloud platforms (AWS, GCP)
Containerized environments (Docker)
Research, Collaboration & Documentation
Conduct literature reviews in soft matterQuantum algorithmsDesign, execute, and analyze numerical experimentsPrepare:
Technical reportsInternal whitepapersPresentations and datasetsCollaborate closely with:Quantum hardware teamsAlgorithm developersMolecular Dynamics & Classical Simulations
Classical MD simulations (LAMMPS, GROMACS)Force-field parameterization & validationReactive force fields (ReaxFF)ML-accelerated MD workflowsParameter generation for coarse-grained simulationsPolymers & Soft Matter Specialization
DFT-based parameter extraction for polymersMultiscale polymer modeling (AA, CG)Dissipative Particle Dynamics (DPD)Monte Carlo SimulationsPolymer blends, Polymer nanocomposites, surfactants, colloidsPolymerization, degradation, crosslinking, morphology and aging studiesIntegration of DFT MD DPD Phase field simulations pipelinesSoftware & Programming Skills
DFT Codes: ORCA, PySCFMD Codes: LAMMPS, GROMACS, NAMD, AMBERProgramming: Python (mandatory), BashInfrastructure: HPC, MPI, Docker, Git, AWS / GCPSoft Skills
Strong analytical and first-principles thinkingAbility to design reproducible scientific workflowsClear scientific communicationHigh ownership and curiosity-driven research mindsetEducational Qualifications
PhD (or pursuing PhD for intern role) in Chemistry, Materials Science, Chemical Engineering, Physics, Computational Science or related STEM fieldStrong foundation in Physical chemistry, Quantum mechanics, Statistical mechanics & thermodynamicsSpecialization in computational chemistry / materials modeling strongly preferredPreferred Qualifications
Publications or strong computational project portfolioExperience with HPC & large-scale simulationsPrior work in: Materials discovery, Polymer modeling, ML-driven materials scienceExposure to quantum algorithms or hybrid quantum-classical workflows Read Less