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- Delft University of Technology (TU Delft); today published
- Delft University of Technology (TU Delft)
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- Delft University of Technology (TU Delft); 16 Oct ’25 published
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mechanical, electrical, software, data science, and AI-expertise. We are seeking highly-motivated candidates for the six PhD positions advertised here that address the challenge above by pushing the boundaries
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mathematical methods, algorithms, and applications are required. Simulators are a recognized method for architectural design explorations and the implementation of software development platforms. The goal
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mathematical methods, algorithms, and applications are required. Simulators are a recognized method for architectural design explorations and the implementation of software development platforms. The goal
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architectural design explorations and the implementation of software development platforms. The goal of this PhD is to create a modular and extensible simulation infrastructure that facilitates design
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Reconfigurable/Spatial computing architectures, such as FPGAs, CGRAs, and AI accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs
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Job description We are looking for an excellent and motivated PhD candidate to join the Microelectronics Department at TU Delft, focusing on the design of innovative power management integrated
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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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group, at TU Delft to pioneer self-organizing edge infrastructure that will define the next generation of Internet architecture. This PhD position offers you the opportunity to shape the future
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EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Teaser Text We invite applications for a fully-funded PhD position in the emerging intersection
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longer sustainable. This project pioneers a new paradigm: we design smart, low-power digital AI co-processors that learn and correct the imperfections of their analog counterparts in real-time. As a PhD