Computational Materials Discovery Scientist

Location: Bengaluru

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
MD Analysis, 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