PhD researcher AI-assisted reverse engineering of integrated circuits; FTE

PhD researcher on AI-assisted reverse engineering of integrated circuits (1 FTE) (23861)

Tilburg University | Tilburg School of Humanities and Digital Sciences

Research Center for Cognitive Science and Artificial Intelligence

is looking for a

PhD researcher on AI-assisted reverse engineering of integrated circuits (1 FTE)

Departments:
Department of Intelligent Systems (DIS) & Department of Computational Cognitive Science (DCS)

Location:

Tilburg

Full‑time gross monthly salary: €3.059 (first year) – €3.881

Contract duration: 12 months + optional 3 years (or 4 years)

Desired starting date:
September 1st, 2026

The Cognitive Science & Artificial Intelligence Research Center at Tilburg University hosts the Secure Hardware Intelligence Extraction and Logic Deconstruction (SHIELD) research project, a scientific initiative focused on AI‑assisted reverse engineering of integrated circuits for hardware assurance and intelligence analysis. The project is conducted within the Deep Learning for Perception and Data Science, Safety and Security research units, in collaboration with the Data Science Centre of Excellence of the Netherlands Defence Academy and the Netherlands Forensic Institute.

The PhD researcher will operate at the intersection of computer vision, micro‑electronics analysis, and hardware security, and will work under the supervision of researchers within the Department of Intelligent Systems.

The PhD researcher will be formally embedded in the Department of Intelligent Systems (DIS) and will collaborate closely with the Department of Computational Cognitive Science (DCS).

The SHIELD project is a collaboration between Tilburg University, the Data Science Centre of Excellence of the Netherlands Defence Academy, and the Netherlands Forensic Institute, combining expertise in artificial intelligence, micro‑electronics analysis, and hardware security. The project focuses on developing AI‑assisted methodologies for reverse engineering integrated circuits in order to support hardware assurance, threat analysis, and intelligence extraction workflows.

The Tilburg PhD researcher will develop a methodological pipeline for analysing high‑resolution microscopy data of integrated circuits, integrating computer vision, deep learning, and logical reconstruction techniques. The research investigates how multimodal imaging modalities — including scanning electron microscopy (SEM), photon emission microscopy (PEM), and focused ion beam (FIB) imaging — can be combined with AI to reconstruct nanoscale chip structures and infer functional behaviour from physical layouts.

The project addresses the challenge of extracting reliable structural and logical information from complex semiconductor architectures under realistic imaging constraints.

Specifically, the researcher will focus on the following three main tasks:

• (a) Developing deep learning pipelines for multilayer segmentation and nanoscale transistor classification from microscopy images;
• (b) Designing graph‑based inference models capable of reconstructing higher‑level logical blocks and identifying anomalous structures indicative of Hardware Trojans;
• (c) Evaluating methodologies for memory extraction, vulnerability analysis, and logical reconstruction using physically grounded image data.

The research explores how AI‑driven analysis can move beyond manual reverse‑engineering workflows by automating feature extraction and structural interpretation while remaining robust to noise, obfuscation, and architectural variability. Because the work operates at the intersection of scientific research and hardware security, the project carefully balances methodological innovation with responsible handling of sensitive datasets and forensic constraints.

The overall goal is to develop a reproducible proof‑of‑concept methodology that enables scalable analysis of integrated circuits for both defensive hardware verification and intelligence‑oriented analysis of captured electronics. The PhD researcher will evaluate the scientific validity and limitations of the proposed methods and contribute to open‑access publications and technical documentation. The SHIELD project…