Computational Materials Discovery Scientist

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 validation
• Monte Carlo (MC) simulations

Multiscale & Mesoscale Modeling

• Coarse-grained modeling for highly heterogeneous systems
• Phase-field modeling

Benchmarking & Validation

• MD engines: LAMMPS, GROMACS, NAMD

Tools

• MDAnalysis, pymatgen, PLUMED, VOTCA, PACKMOL

Simulation Workflow Engineering

• Build reproducible, automated workflows in Python for:

High-throughput materials screening

MD–CG-Mesoscale simulation pipelines

Data extraction & post-processing

• Develop modular tools for:

Parameters generations

HPC clusters

Cloud platforms (AWS, GCP)

Containerized environments (Docker)

Research, Collaboration & Documentation

• Conduct literature reviews in soft matter
• Quantum algorithms
• Design, execute, and analyze numerical experiments

Prepare:

• Technical reports
• Internal whitepapers
• Presentations and datasets
• Collaborate closely with:
• Quantum hardware teams
• Algorithm developers

Molecular Dynamics & Classical Simulations

• Classical MD simulations (LAMMPS, GROMACS)
• Force-field parameterization & validation
• Reactive force fields (ReaxFF)
• ML-accelerated MD workflows
• Parameter generation for coarse-grained simulations

Polymers & Soft Matter Specialization

• DFT-based parameter extraction for polymers
• Multiscale polymer modeling (AA, CG)
• Dissipative Particle Dynamics (DPD)
• Monte Carlo Simulations
• Polymer blends, Polymer nanocomposites, surfactants, colloids
• Polymerization, degradation, crosslinking, morphology and aging studies
• Integration of DFT → MD → DPD→Phase field simulations pipelines

Software & Programming Skills

• DFT Codes: ORCA, PySCF
• MD Codes: LAMMPS, GROMACS, NAMD, AMBER
• Programming: Python (mandatory), Bash
• Infrastructure: HPC, MPI, Docker, Git, AWS / GCP

Soft Skills

• Strong analytical and first-principles thinking
• Ability to design reproducible scientific workflows
• Clear scientific communication
• High ownership and curiosity-driven research mindset)

Educational Qualifications

• PhD (or pursuing PhD for intern role) in Chemistry, Materials Science, Chemical Engineering, Physics, Computational Science or related STEM field
• Strong foundation in Physical chemistry, Quantum mechanics, Statistical mechanics & thermodynamics
• Specialization in computational chemistry / materials modeling strongly preferred

Preferred Qualifications

• Publications or strong computational project portfolio
• Experience with HPC & large-scale simulations
• Prior work in: Materials discovery, Polymer modeling, ML-driven materials science
• Exposure to quantum algorithms or hybrid quantum–classical workflows