Physics AI Internship (Summer 2027)

Synopsys•Sunnyvale, CA

About The Position

Synopsys is seeking an intern to conduct research at the intersection of deep learning, scientific computing, and foundation models for physics applications. The intern will contribute to developing Large Physics Foundation Models (LPFMs), including exploring architectures and training strategies, and will build and curate large-scale physics datasets and supporting research infrastructure. The role involves collaborating with researchers and engineers, presenting findings, participating in knowledge-sharing, and helping shape AI for the physical sciences. The internship is 3–4 months, based in Sunnyvale, California, and may be in-office, hybrid, or remote. It is full-time, with a planned start in May or June 2027.

Requirements

Currently pursuing a master's or PhD degree in Physics, Computer Science, Engineering, or a related field.
Strong passion for applying AI and deep learning to scientific or physics-based problems.
Experience with scientific computing, machine learning frameworks, and large-scale data management.
Excellent problem-solving, communication, and collaboration skills.
Self-motivated, creative, and eager to contribute to high-impact research and engineering projects.

Responsibilities

Engage in research at the intersection of deep learning, scientific computing, and foundation models for physics applications.
Contribute to Large Physics Foundation Model development by exploring novel architectures and training strategies.
Build and curate large-scale physics datasets and design infrastructure for data-driven AI research in the physical sciences.
Collaborate with cross-functional teams of researchers and engineers.
Present findings, participate in knowledge-sharing sessions, and help shape the direction of AI for the physical sciences.