Job Description
The role involves conducting atomistic simulations and quantum modeling of battery materials, generating data for AI-driven materials discovery, and collaborating with experimental teams to advance next-generation battery technologies using computational and machine learning techniques.
Key Responsibilities
- Perform and oversee DFT, MD, and QM simulations of battery components such as electrolytes, coatings, and electrodes.
- Develop and refine machine learning-enhanced force fields and surrogate models to accelerate simulation efforts.
- Generate structured simulation data for training AI property prediction models and material screening.
- Automate complex simulation workflows to improve efficiency and scalability.
- Collaborate with experimental teams to validate models and inform design iterations.
Requirements
- Ph.D. in Mechanical Engineering, Materials Science, Chemical Engineering, or a closely related computational physics field.
- Deep and extensive experience in atomistic simulation and quantum modeling, including proficiency with key QM DFT tools VASP, Quantum Espresso and MD simulations.
- Strong background in electrochemical energy materials and extensive computational work focused on batteries and fuel cells.
- Strong coding skills in Python along with related libraries like Pandas and TensorFlow for simulation workflow automation and data analysis.
- Experience in developing or utilizing ML-enhanced force fields and surrogate models for materials prediction, or equivalent practical experience.
- Ability to conduct and oversee DFT (Density Functional Theory), MD (Molecular Dynamics), and QM (Quantum Mechanics) simulations of battery components, including electrolytes, coatings, and electrodes.
- Ability to develop and refine ML-enhanced force fields and surrogate models to accelerate simulation time scales and enable multi-scale simulation efforts.
- Experience in generating high-quality, structured simulation data to serve as training sets for AI property prediction models and material screening modules.
- Proficiency in automating complex simulation workflows using strong coding practices to enhance efficiency and scalability.
- Ability to collaborate with experimental teams, leveraging a hybrid computational experimental literacy to validate models and drive design iteration.
- Familiarity with advanced simulation tools such as VASP, Quantum Espresso, and data science libraries like TensorFlow and Pandas to manage and analyze large datasets.
Benefits & Perks
highly competitive salary
robust benefits package including comprehensive health coverage
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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