PhD Researcher on AI-Assisted Reverse Engineering of Integrated Circuits
Develop AI-driven methodologies for hardware assurance and reverse engineering of integrated circuits.
Best for:Best for researchers with a strong background in computer vision, deep learning, and an interest in hardware security.
Overview
Tilburg University is seeking a PhD researcher to join the SHIELD project, a collaborative initiative focused on AI-assisted reverse engineering of integrated circuits. The project aims to develop automated pipelines for analyzing high-resolution microscopy data to support hardware assurance and threat analysis. Working within the Department of Intelligent Systems, the successful candidate will integrate computer vision, deep learning, and logical reconstruction techniques. This role involves close collaboration with the Netherlands Defence Academy and the Netherlands Forensic Institute to ensure research remains grounded in real-world forensic workflows. The position offers an opportunity to contribute to open-access publications while addressing critical challenges in semiconductor security.
What you'll do
- Develop deep learning pipelines for multilayer segmentation and transistor classification from microscopy images.
- Design graph-based inference models to reconstruct logical blocks and identify potential Hardware Trojans.
- Evaluate methodologies for memory extraction and vulnerability analysis using physically grounded image data.
- Collaborate with forensic and defence partners to validate research methods under practical constraints.
- Contribute to open-access scientific publications and technical documentation.
- Evaluate the scientific validity and limitations of proposed AI-driven reverse-engineering workflows.
Requirements
Degree required: Master’s degree in Artificial Intelligence, Computer Science, Electrical Engineering, Computational Neuroscience, Physics, or a closely related field.
Degree offered: PhD
Language: English; high proficiency in academic writing and speaking.
Instruction: English
- Strong programming proficiency in Python.
- Demonstrated experience with deep learning frameworks such as PyTorch or TensorFlow.
- Experience in computer vision and image analysis.
- Strong analytical and problem-structuring skills for complex systems.
- Excellent written and verbal communication skills in English.
- Must hold a completed university master’s degree in a relevant quantitative field.
Funding & terms
How to apply
Deadline: 2026-11-01
Interested candidates should submit their application through the official university portal. Ensure all required academic documentation is included to support your candidacy.
- CV
- Cover letter
- Academic transcripts
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