ML-Driven Energy Materials Innovation Postdoctoral Fellow

About the Role

Our team is searching for a creative and agile researcher who thrives at rapid iteration between ambitious ideas in machine learning-driven materials discovery. The ideal candidate can swiftly prototype and test high-risk, high-reward hypotheses, with the aim of advancing the limits of next-generation energy storage. Although experience in energy storage is desirable, we encourage specialists in computational materials science who can apply their skills to energy applications and expedite discovery through AI-driven methods.

Responsibilities

• Drive innovation in machine learning-driven energy materials discovery by rapid prototyping and testing of bold hypotheses. 
• Develop quick-turnaround solutions using hybrid ML and computational chemistry methods, pursuing high-risk, high-reward research focused on energy storage. 
• Design and refine automated workflows for high-throughput materials screening. 
• Translate moonshot ideas into actionable research trajectories, effectively communicating insights to diverse audiences, from experts to non-experts. 
• Remain up to speed with emerging AI techniques, rapidly adapting external advancements to the materials science domain as appropriate. 
• Collaborate effectively with cross-functional teams of engineers, scientists, and domain experts to innovate, refine, or develop cutting-edge ideas and algorithms.
• Conduct large-scale synthetic data generation campaigns using simulation tools (DFT, MD, physics-based models) to study energy storage materials (interfacial stability, ion transport, degradation). 
• Publish findings in high-impact journals and present at conferences.

Qualifications

• Currently enrolled or recently completed a STEM PhD Program in Materials Science, Mechanical Engineering, Physics, Chemistry, Computational Science, or related fields.
• Extensive experience applying both (classical or ML-based) molecular dynamics and density functional theory for energy storage applications.
• Experience modeling electrochemical energy storage systems and strong understanding of their degradation mechanisms.
• Expertise in developing data pipelines for computational chemistry experiments.
• Preferred candidates have domain knowledge in battery materials, particularly for lithium-ion and other cell chemistries (e.g., cathodes, anodes, electrolytes).
• Fluency with Python and Linux.
• Strong research track record, evidenced by high-impact publications or relevant project contributions.

Details

• Start date: Year-round, on a rolling basis
• Duration: up to 1 year, with possibility of renewal
• Location: Remote

The US base salary range for this full-time position is expected to be $115k - $135k per year. Our salary ranges are determined by role and level. Within the range, individual pay is determined by factors including job-related skills, experience, and relevant education or training. This role may be eligible for annual discretionary bonuses and equity.