Master Thesis Proposal - AI & Vision for PCB Defect Detection

Do you want to explore the intersection of computer vision, AI, and embedded edge computing?

In this thesis, we invite one or two students to develop methods for automatic defect detection on printed circuit boards (PCBs) using machine learning and real-world vision data. The project combines inference on both NVIDIA Jetson and a high-performance workstation, with training offloaded to the workstation, enabling efficient deployment in industrial production environments.

About The Project

The aim is to design and evaluate an AI-based system for PCB defect detection. Within this scope, you will:

• Work with existing PCB image datasets.
• Train and benchmark different AI models (e.g. CNNs, transformer-based networks).
• Implement inference on NVIDIA Jetson for real-time defect detection.
• Offload model training and refinement to a workstation environment.
• Analyze system performance in terms of accuracy, latency, and resource efficiency.

Possible Research Questions

• Which AI architectures are most effective for detecting visual PCB defects?
• How can inference on edge hardware be optimized for speed and accuracy?
• How well can such a system generalize to unseen PCB types and defect classes?

Features & Technologies

• Computer vision & deep learning (PyTorch, TensorFlow, OpenCV)
• Edge inference on NVIDIA Jetson
• GPU-accelerated training on workstation hardware
• Embedded AI and system integration
• Industrial vision datasets

Who Are You?

We are looking for one or two master’s students with an interest in AI, computer vision, and embedded systems. Experience with Python and machine learning frameworks is valuable, but the most important is that you are curious, motivated, and eager to explore AI in a real-world context.

To give you the best possible support during your thesis, we’d like you to be able to come to the office connected to the project and spend most of your time working from there.

Application:

We look forward to receiving your resume, and preferably, a personal letter in which you explain why you want to write your thesis with Syntronic.

We screen and evaluate applications on an ongoing basis. The thesis project may be filled before the application deadline.