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5 Sep 2026 Job Information Organisation/Company Tilburg University Research Field Computer science » Computer hardware Computer science » Programming Engineering » Electrical engineering Engineering
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architectures, neuromorphic computing, hardware security, and energy-efficient computer systems. The candidate will become part of the QuNeCo project, an ambitious collaboration between universities in
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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
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and/or the analysis and processing of sensor data, for example building and deploying sensing systems (wearable, ambient, RF, distributed), edge and on-device data processing and machine learning
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, enabling trustworthy computation without compromising energy budgets. Treating security as a first-class citizen when designing hardware components and future computer architectures requires novel design
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, training, verification and validation of machine learning algorithms on hardware platforms for space or terrestrial applications; Knowledge of model-based system engineering tools and methodologies
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hardware prototypes. Collaborate with interdisciplinary research teams spanning artificial intelligence, circuit design, computer architecture, and embedded systems engineering.
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goals will also be explored during the later stages of the selection process. You should also have: solid knowledge of artificial intelligence and machine learning techniques, including their limitations
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Design and build flow setups using 3D printer, pumps, valves operated by a computer and the corresponding software. Develop flow cells to connect various spectroscopic tools to the setup. Create and
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, pumps, valves operated by a computer and the corresponding software Develop flow cells to connect various spectroscopic tools to the setup Create and validate reproducible automated workflows