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?
Join SES and make an impact in renewable energy
Stay Updated on Sustainability Jobs
Get the latest renewable energy jobs and career tips delivered to your inbox.
Job Alerts
Get notified about new sustainability jobs
More at SES
More jobs at SES
Senior Scientist, Electrolyte
SES
Woburn
Full Time
Feb 24
Business Development Representative BDR
SES
Woburn
ON-SITE
Jan 29
Materials Business Unit Head
SES
Woburn
Full Time
Jan 21
More jobs in Woburn, Massachusetts
Senior Staff Accountant
Unison
Woburn
Full Time
Jan 28
Accounts Payable Coordinator - Temporary to Permanent
Unison
Woburn
TEMPORARY_TO_PERMANENT
Dec 16
Accounts Payable Coordinator - Temporary to Permanent
Unison
Woburn
TEMPORARY
Jan 1