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AI-Driven Physical Design Engineering, PhD Intern
Intel Corporation. Research and implement AI/ML techniques to improve physical design processes and methodologies .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in AI/ML techniques for physical design processes, with strong proficiency in Python and experience in developing intelligent automation frameworks. Familiarity with Graph Neural Networks and industry-standard physical design tools is essential for optimizing design methodologies.
Highest-signal resume keywords
Python ProgrammingGraph Neural NetworksAI/ML TechniquesEDA ToolsDigital Design Concepts
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI/ML TechniquesGraph Neural NetworksDigital DesignFlow AutomationReinforcement LearningData AnalysisPhysical Design ToolsSynthesisPlace and RouteTiming Analysis
Tools & Technologies
TensorFlowPyTorchPandasNumPyMatplotlibTCLPerl
Industry Keywords
Electrical EngineeringComputer EngineeringComputer SciencePhysical DesignRTL DesignVerificationPPA Improvements
Tech Stack
Tools & technologiesNumpyPandasPerlPythonPyTorchTensorflow
About the role
Key responsibilities & impact- Research and implement AI/ML techniques to improve physical design processes and methodologies
- Develop AI-enhanced workflows for high-performance silicon implementation
- Build ML-powered data analysis and summarization pipelines
- Design and implement Graph Neural Network-based systems for intelligent design execution
- Develop intelligent automation frameworks using Python, TensorFlow or PyTorch, TCL, and Perl
- Collaborate with AI research and design engineering teams
- Evaluate and benchmark AI-driven methodologies against traditional approaches for PPA improvements
Requirements
What you’ll need- Enrolled in a PhD program in Electrical Engineering, Computer Engineering, Computer Science, or a related field
- Proficiency in industry-standard physical design tools, including synthesis, place and route, and timing analysis software
- Familiarity with Python or TCL for flow automation and debugging
- Understanding of digital design concepts and methodologies, including RTL design and verification
- Understanding of Graph Neural Networks (GNNs) or graph-based algorithms
- Experience with Pandas, NumPy, and Matplotlib
- Knowledge of reinforcement learning concepts for design optimization
- Strong Python programming skills with ML application experience
- Background in EDA tools and physical design fundamentals
Benefits
Comp & perks- Competitive pay
- Stock bonuses
- Health benefits
- Retirement benefits
- Vacation benefits