Research Engineer

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