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this PhD project, you will investigate the co-design between event-based learning algorithms and neuronal hardware units with multi-scale time constants. The algorithmic methodology will exploit recent
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Software. It is a collaboration between the University of Amsterdam and the Dutch Centre for Mathematics and Computer Science (CWI). QuSoft’s mission is to develop new protocols, algorithms and applications
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of machine learning (ML) and quantum many-body physics. We are also happy to work with experts in one of the two fields who are committed to learning the other. Moreover, we look for interest in developing
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Research Infrastructure? No Offer Description Job description You will explore hardware/algorithm co-design for NeuroAI, simultaneously taking into account scalability inspired by modern AI workloads, as
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image processing and analysis method development. The position builds on the lab's track-record in the field of computational imaging techniques for super-resolution microscopy and image analysis
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the security and cyber domain, with special focus on ethics and algorithmic transparency; Human-Technology Interaction: Developing robots, simulations, and games, which use a variety of AI technologies to learn
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interpret the reliability of underground maps? Your Role As an EngD candidate, you will work under supervision to develop a data-driven model for assessing the reliability of official network maps (KLIC
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Join TU Delft and work together with NXP to build low-power AI accelerators for self-healing analog/RF calibration, fixing noise/offset. Co-design algorithms & hardware and validate on real silicon
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transportation systems may include a fleet autonomous cars, vans, and buses. This PhD position within FlexMobility will focus on the underlaying assignment and routing algorithms for real-time operation of
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within FlexMobility will focus on the underlaying assignment and routing algorithms for real-time operation of the vehicle fleet and the multi-objective design of the mixed transporation network. Our key