Research Engineer - Battery Modeling

Research Engineer - Battery Modeling

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

Key Responsibilities Develop and implement machine learning models for battery material interface andtransport phenomena. Integrate multi-scale modeling approaches spanning quantum chemistry, moleculardynamics, 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