Graduate Student Intern Software Engineering

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 Masters 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.