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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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hardware prototypes. Collaborate with interdisciplinary research teams spanning artificial intelligence, circuit design, computer architecture, and embedded systems engineering.
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into fewer, far more reliable logical ones. Without effective QEC, adding qubits does not add computing power, because errors accumulate faster than machines can scale. Despite impressive hardware progress
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-accelerated, for self-aware neuromorphic system-on-chip (SoC) architectures, enabling fast and accurate exploration of emerging hardware and learning paradigms. The framework will support rapid prototyping
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imaging. Experience with scientific programming (e.g., MATLAB, Python and/or C++). Excellent analytical and problem-solving skills. Interest in image reconstruction, beamforming, machine learning, and
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adaptive resource allocation, AI-driven network orchestration, dynamic beam steering, joint communication and sensing, and cross-layer optimization. Machine learning techniques will be investigated
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Are you interested in exploring how multi-agent aerial manipulation can contribute to construction and working at the intersection of robotics and machine learning? Job description Advancements in