QpiVolta Technologies | Research Engineer

1 month ago


india QpiVolta Technologies Full time

Research Engineer - Battery Modeling

Position Overview

QpiVolta Technologies is seeking a Research Engineer to work on accelerating battery modeling through advanced Machine Learning techniques. The ideal candidate will have a strong background in both computational chemistry and machine learning, with experience in multi-scale modeling of materials and interfaces.

Key Responsibilities

  • Develop and implement machine learning models for battery material interface and transport phenomena.
  • Integrate multi-scale modeling approaches spanning quantum chemistry, molecular dynamics, and continuum models.
  • Apply and fine-tune ML force fields for accurate materials simulation.
  • Contribute to the development of battery design and optimization workflows.
  • Collaborate with interdisciplinary teams on battery modeling projects.

Required Qualifications

  • Master’s degree in Chemistry, Materials Science, Chemical Engineering, Mathematics, Physics, or a related field.
  • Experience applying Large Language Models in Scientific Domains
  • Strong background in computational modeling at multiple scales:

○ Density Functional Theory (DFT)

○ Molecular Dynamics (MD)

○ Coarse-grained modeling

○ Continuum modeling

  • Experience with relevant software tools:

○ LAMMPS for molecular dynamics simulations

○ DFT software packages (e.g., VASP, Quantum ESPRESSO, or similar)

○ PyBaMM, Battery Design Studio (Python Battery Mathematical Modelling)

○ Battery design software tools

Technical Skills

  • Demonstrated experience in:

○ Machine learning model development and implementation

○ Force field development and fine-tuning

○ Integration of multi-scale modeling approaches

○ Python programming and scientific computing libraries

○ Version control systems (e.g., Git)

Battery Modeling Workflow Experience

  • Proficiency in electrochemical modeling workflows:

○ P2D (pseudo-two-dimensional) models for cell-level simulation

○ SPM (single particle model) for simplified cell analysis

○ Newman model implementation and modification

○ Electrode-scale transport phenomena modeling

  • Familiarity with multi-physics coupling approaches:

○ Thermal-electrochemical coupling

○ Mechanical-electrochemical coupling

○ Aging mechanisms integration

  • Experience with automated workflow tools:

○ Battery parameter estimation pipelines

○ Materials screening workflows

○ Automated DFT calculation setup

○ High-throughput simulation management

  • Understanding of different modeling scales:

○ Atomistic simulations for interface phenomena

○ Mesoscale modeling for particle interactions

○ Cell-level performance prediction

○ Pack-level thermal and electrical behavior

Preferred Qualifications

  • Previous research experience in battery materials or electrochemistry
  • Publications or contributions to papers/open source in relevant fields
  • Experience with high-performance computing environments
  • Knowledge of electrochemical characterization techniques

Required Competencies

  • Strong analytical and problem-solving skills
  • Excellent programming and data analysis capabilities
  • Ability to work independently and as part of a team
  • Strong written and verbal communication skills
  • Strong programming skills preferably in Python
  • Experience with scientific documentation and technical writing

Project Focus Areas

  • Battery material interface modeling
  • Transport phenomena simulation
  • Stability analysis across multiple scales
  • ML-accelerated materials discovery
  • Integration of quantum, molecular, and continuum approaches
  • Workflow optimization and automation



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