QpiVolta Technologies | Research Engineer
1 month ago
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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