PhD Residency - Battery Materials Discovery

Summary

Join SandboxAQ's Residency Program as a creative and agile researcher to contribute to machine learning-driven materials discovery for next-generation energy storage. The role involves conducting large-scale synthetic data generation, training machine learning models, and collaborating with cross-functional teams. You will iterate on machine learning solutions, publish findings, and present at conferences. The ideal candidate possesses a PhD in a relevant field, extensive experience in machine learning and simulations, and fluency in Python and relevant scientific computing libraries. The position offers a competitive salary, benefits, and opportunities for professional development. The program is paid and offers flexible duration.

Requirements

• 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)

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

Preferred Qualifications

• 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

Benefits

• Medical/dental/vision
• Family planning/fertility
• PTO (summer and winter breaks)
• Financial wellness resources
• 401(k) plans