Job Description
The role involves conducting advanced atomistic simulations and quantum modeling to accelerate the discovery of electrochemical energy materials, integrating AI techniques to generate training data and improve battery technologies within a collaborative, innovative environment.
Key Responsibilities
- Conduct and oversee DFT, MD, and QM simulations of battery components such as electrolytes, coatings, and electrodes.
- Develop and refine ML-enhanced force fields and surrogate models to accelerate simulation efforts.
- Generate high-quality 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 hybrid computational and experimental literacy to validate models and drive design iteration.
- Familiarity with advanced simulation tools such as VASP and 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
dynamic, collaborative, and innovative work 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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