PhD Residency - Battery Materials Discovery

Residencies at SandboxAQ

The SandboxAQ Residency Program is an excellent, paid opportunity for graduate students to have industrial impact. Residents work within all of the teams at SandboxAQ on a variety of projects, and each have a unique experience depending on the work their team is focused on, as well as their own interests, skills, and goals. During their time with the program, we encourage residents to contribute to publications, attend conferences, and pursue their professional development. In addition to technical work, community is very important to us and we provide space and events to network and socialize with others in the program, as well as the SandboxAQ community as a whole.

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.

Responsibilities

• Conduct large-scale synthetic data generation campaigns using simulation tools (DFT, MD, physics-based models) to study energy storage phenomena like interfacial stability, ion transport, and degradation.
• Train and implement machine learning interatomic potentials to predict battery material properties.
• Remain up to speed with emerging AI techniques, adapting external advancements to the materials science domain as appropriate. 
• Rapidly iterate on machine learning solutions, demonstrating the ability to pivot and adapt methodologies as research challenges evolve.
• Collaborate effectively with cross-functional teams of engineers, scientists, and domain experts to innovate, refine, or develop cutting-edge ideas and algorithms.
• Publish findings in high-impact journals and present at conferences.

Qualifications

• Currently enrolled in a PhD program in Materials Science, Mechanical Engineering, Physics, Chemistry, Computational Science, or related fields.
• Experience performing density functional theory and/or molecular dynamics simulations for energy storage applications.
• Extensive experience training/tuning machine learning interatomic potentials and performing large scale inference campaigns.
• Fluency with Python, Linux, and relevant scientific computing libraries (e.g., ASE, Pymatgen, Scikit-learn, PyTorch).
• Proven ability in designing and managing sophisticated computational chemistry data pipelines, particularly across large-scale compute resources (GPU/CPU clusters).
• Preferred candidates have domain knowledge in battery materials, particularly for lithium-ion and other cell chemistries (e.g., cathodes, anodes, electrolytes).
• Strong research track record, evidenced by high-impact publications or relevant project contributions.

Other Details

• Start date: Year-round, on a rolling basis
• Duration: flexible

The US base salary range for this full-time position is expected to be $125k - $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.