Graduate AI/ML Intern (Materials Science)

Job Description

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

The Tone:
This is a year-round, graduate internship at Sandia National Laboratories, offering a hybrid work arrangement with required onsite presence when necessary in Albuquerque, NM. Sandia is the nation’s premier science and engineering lab, dedicated to national security and technology innovation across a broad range of areas. In this role, you will develop and apply novel AI/ML techniques in materials science applications, making a tangible impact on materials discovery efforts.

The TL;DR
• Role: Internship
• Type: Temporary
• Location: Hybrid – Albuquerque, NM

• Team: Materials Physics department
• Mission: Develop and apply AI/ML techniques to automate materials science processes for energy needs and accelerate materials modeling.
• Tech Stack: pytorch, numpy, ase, pymatgen, Materials Project, OpenCV

What You’ll Actually Do
• Automate Analysis: Develop AI/ML models capable of automating the analysis of complex materials science datasets to understand synthesized materials composition and structure.
• Extract Data: Develop AI/ML models capable of automating the extraction of legacy, document-centric materials data to provide training databases or knowledge graphs.
• Manage Data: Utilize open-source tools and high-performance computing resources to efficiently store and access large amounts of data.
• Utilize Libraries: Be familiar with and efficiently utilize open-source materials and computer science libraries and databases like ase, pymatgen, Materials Project, and OpenCV.
• Collaborate Research: Conduct research that requires extensive and effective collaboration and communication with team members engaged in modeling, material synthesis, and advanced characterization.

The Must-Haves
• Background: Currently attending and enrolled full time in an accredited science, engineering, or math graduate program, having earned a bachelor’s degree, and maintaining a minimum cumulative GPA of 3.0/4.0.
• Experience: Extensive experience in software development and scientific computing in Python, utilizing standard machine learning tools like pytorch and numpy.
• Skills: AI/ML model development, materials science data analysis, high-performance computing (HPC) utilization, effective collaboration and communication.
• Bonus: Experience with HPC systems and the use of GPU clusters for AI/ML model training, validation, and inference; some experience with hardware programming; some experience with applying AI/ML to materials science or chemistry-related problems.