AI/ML Graduate Intern (Materials Science)

Job Description

AI/ML Graduate Intern (Materials Science) | Sandia National Laboratories

The Tone:
This is a year-round graduate internship at Sandia National Laboratories, offering a flexible work arrangement that combines onsite and offsite work, requiring residency within a reasonable commuting distance to the assigned location. Sandia National Laboratories is the nation’s premier science and engineering lab, focused on national security and technology innovation. This role is dedicated to developing and applying advanced AI/ML techniques to novel materials science challenges, directly impacting critical applications such as materials for hydrogen production, storage, and energy generation. The successful intern will make tangible contributions to materials discovery efforts, with opportunities for academic publications and a significant impact on national security.

The TL;DR
• Role: Internship
• Type: Temporary, Part-Time (up to 30 hours/week during academic year, up to 40 hours/week during summer)
• Location: Hybrid; reasonable commuting distance to assigned work location
• Team: Materials Physics department
• Mission: Develop and apply cutting-edge AI/ML techniques for automated information extraction, materials characterization, and accelerated materials modeling to advance materials discovery.
• Tech Stack: `ase`, `pymatgen`, `Materials Project`, `OpenCV`, PyTorch, NumPy, Python, HPC, GPU clusters

What You’ll Actually Do
• AI/ML Development: Develop AI/ML models designed to automate the analysis of complex materials science datasets, aiming to understand synthesized materials’ composition and structure.
• Data Automation: Create AI/ML models capable of automating the extraction of legacy, document-centric materials data to build robust training databases or knowledge graphs for subsequent use.
• Resource Utilization: Efficiently utilize open-source tools and high-performance computing (HPC) resources for storing and accessing large volumes of data.
• Library Expertise: Demonstrate familiarity with and efficiently leverage open-source materials and computer science libraries and databases, including but not limited to `ase`, `pymatgen`, `Materials Project`, and `OpenCV`.
• Research & Collaboration: Conduct impactful research that necessitates extensive and effective collaboration and communication with diverse team members involved in modeling, material synthesis, and advanced characterization.

The Must-Haves
• Background: Possess an earned bachelor’s degree; currently attending and enrolled full-time in an accredited science, engineering, or math graduate program.
• Experience: Ability to work up to 30 hours per week during the academic year and up to 40 hours per week during the summer. Applicants must be legally authorized to work in the United States of America.
• Skills: Maintain a minimum cumulative GPA of 3.0/4.0.
• Bonus: Enrolled in a post-undergraduate program (Master’s or Ph.D.). Extensive experience in software development, scientific computing in Python, and proficiency with standard machine learning tools (e.g., PyTorch, NumPy). Experience with High-Performance Computing (HPC) systems and effective use of GPU clusters for AI/ML model training, validation, and inference. Some experience with hardware programming is considered a plus, as is prior experience applying AI/ML techniques to materials science or chemistry-related problems.