AI-Driven Physical Design Engineering (PhD Intern)

Research and implement AI/ML methods and intelligent automation workflows to improve physical design processes, reduce manual effort, and optimize silicon implementation. Build data-analysis pipelines and GNN-based decision systems, collaborate with AI and design teams, and benchmark results against traditional methods using PPA metrics.

Responsibilities

• Research and implement AI/ML methods and intelligent automation workflows to improve physical design processes, reduce manual effort, and optimize silicon implementation. Build data-analysis pipelines and GNN-based decision systems, collaborate with AI and design teams, and benchmark results against traditional methods using PPA metrics.

Requirements

• Applicants must be enrolled in a PhD program in Electrical Engineering, Computer Engineering, Computer Science, or a related field, and have familiarity with physical design tools, Python or TCL scripting, and digital design concepts including RTL design and verification. Preferred qualifications include GNNs or graph algorithms, data-analysis and ML tools, reinforcement learning, strong Python skills, and physical design or EDA experience.

Benefits

• Competitive Pay
• Stock Bonuses
• Health Benefits
• Retirement Benefits
• Vacation

Key Skills