Research Engineer - Battery Modeling

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

Masters 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