Job Description
The role involves developing and applying advanced physics-based simulation techniques, such as reactive force fields and DFT coupling, to model complex battery materials, generate high-quality datasets for AI-driven materials discovery, and collaborate across computational and experimental teams to accelerate battery technology innovation.
Key Responsibilities
- Develop and apply advanced physics-based simulation techniques, including Reactive Force Fields (ReaxFF) and MD-DFT coupling, to model complex materials phenomena.
- Conduct multi-scale computational projects from atomistic to mesoscale modeling, utilizing tools like VASP, LAMMPS, GROMACS, and CP2K.
- Generate, validate, and analyze atomistic and mesoscale datasets for training machine learning potentials and AI-driven materials discovery workflows.
- Automate and optimize simulation workflows through coding in Python, C++, or FORTRAN to improve computational efficiency.
- Perform physics-based validation of simulation outputs to ensure accuracy and reliability for materials modeling.
Requirements
- Ph.D. in Computational Chemistry of Energy Materials, Materials Science, or Computational Physics.
- Deep, demonstrated expertise in advanced physics-based simulation, including proficiency with DFT, MD, and Quantum Mechanics (QM) simulation tools.
- Proven experience with Reactive Force Fields ReaxFF and methods for MD DFT coupling.
- Strong coding ability in languages such as Python, C, or FORTRAN, and deep practical experience with simulation codes like VASP, LAMMPS, GROMACS, or CP2K.
- Solid understanding of battery chemistry modeling and experience in generating and validating atomistic and mesoscale data.
- Ability to develop and apply advanced simulation techniques, specializing in Reactive Force Fields ReaxFF and MD DFT coupling, to model complex materials phenomena.
- Experience conducting and analyzing multi-scale computation projects, covering atomistic simulation up to mesoscale modeling.
- Ability to utilize and maintain advanced simulation tools, including VASP, LAMMPS, GROMACS, and CP2K, for high-fidelity simulations.
- Perform rigorous physics-based validation on all simulation outputs to ensure accuracy and reliability.
- Generate and validate high-quality atomistic and mesoscale datasets used for training ML potential and other scientific AI models.
- Develop advanced simulation workflows and contribute to the creation of ML potentials and AI-driven materials discovery tools.
- Apply strong coding skills to automate complex simulation workflows and enhance computational efficiency.
- Maintain a solid understanding of battery chemistry modeling and collaborate effectively across computational and experimental teams.
Benefits & Perks
A highly competitive salary
Robust benefits package, including comprehensive health coverage
An attractive equity stock options program
Opportunity to contribute to meaningful scientific projects with broad public impact
Work in a dynamic, collaborative, and innovative environment
Significant opportunities for professional growth and career development
Access to state-of-the-art facilities and proprietary technologies
Ready to Apply?
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