Cadence

Graduate Student Intern - Software Engineering

USA internshipsSoftwarePosted 1 month ago

About this role

From Cadence’s careers page.

Responsibilities

Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
Research and develop AI-driven approaches for geometry modeling, mesh generation, and topology optimization workflows.
Prototype and evaluate AI-assisted methods for automating geometry creation and simulation model preparation.
Work with researchers and engineers to integrate AI technologies into engineering and physics-based applications, including thermal and structural simulation.
Analyze experimental results and improve the quality, robustness, and performance of AI-generated geometry and mesh models.
Investigate methods to reduce manual modeling effort and accelerate design and simulation workflows through AI automation.
Contribute to technical discussions, documentation, research reports, and prototype software development.

Basic Qualifications

Currently pursuing a Master's degree or PhD in Computer Science, Engineering, Applied Mathematics, or a related field.
Strong foundation in data structures, algorithms, and software engineering principles.
Programming experience in C/C++ and Python.
Familiarity with software development practices, including debugging, testing, and version control.
Strong analytical, problem-solving, collaboration, and communication skills.
Curiosity and enthusiasm for applying AI technologies to engineering problems.

Preferred Qualifications

Experience with AI/ML, including deep learning, LLMs, GenAI, or GNNs.
Familiarity with geometric modeling, mesh generation, retopology, computational geometry, or graph-based representations.
Coursework or research experience in computer graphics, computer-aided engineering (CAE), scientific computing, or simulation.
Exposure to CAD, CAE, EDA, or simulation-driven design applications.
Interest in topology optimization, geometry processing, performance optimization, parallel computing, or GPU acceleration.
Experience with machine learning frameworks such as PyTorch, TensorFlow, or similar tools.

Apply nowOpens Cadence’s application page

About this role

From Cadence’s careers page.

Responsibilities

Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
Research and develop AI-driven approaches for geometry modeling, mesh generation, and topology optimization workflows.
Prototype and evaluate AI-assisted methods for automating geometry creation and simulation model preparation.
Work with researchers and engineers to integrate AI technologies into engineering and physics-based applications, including thermal and structural simulation.
Analyze experimental results and improve the quality, robustness, and performance of AI-generated geometry and mesh models.
Investigate methods to reduce manual modeling effort and accelerate design and simulation workflows through AI automation.
Contribute to technical discussions, documentation, research reports, and prototype software development.

Basic Qualifications

Currently pursuing a Master's degree or PhD in Computer Science, Engineering, Applied Mathematics, or a related field.
Strong foundation in data structures, algorithms, and software engineering principles.
Programming experience in C/C++ and Python.
Familiarity with software development practices, including debugging, testing, and version control.
Strong analytical, problem-solving, collaboration, and communication skills.
Curiosity and enthusiasm for applying AI technologies to engineering problems.

Preferred Qualifications

Experience with AI/ML, including deep learning, LLMs, GenAI, or GNNs.
Familiarity with geometric modeling, mesh generation, retopology, computational geometry, or graph-based representations.
Coursework or research experience in computer graphics, computer-aided engineering (CAE), scientific computing, or simulation.
Exposure to CAD, CAE, EDA, or simulation-driven design applications.
Interest in topology optimization, geometry processing, performance optimization, parallel computing, or GPU acceleration.
Experience with machine learning frameworks such as PyTorch, TensorFlow, or similar tools.