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- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); yesterday published
- European Space Agency
- Delft University of Technology (TU Delft); Delft
- Delft University of Technology (TU Delft); 17 Oct ’25 published
- Delft University of Technology (TU Delft); today published
- Delft University of Technology (TU Delft); 16 Oct ’25 published
- University of Groningen
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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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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description AI-enabled polymer monitoring via multi-sensor intelligent non
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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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electrical power, enabling smart sensors to operate without batteries. You will explore novel capacitor-based rectifier architectures, adaptive impedance-matching algorithms, and on-chip protection mechanisms
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on sufficient and sufficiently clean water. However, we often lack the data to fully understand the dynamics of contaminants throughout the urban water cycle. Existing sensors for water quality monitoring do not
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to track how the prevalences of different strains in a mixed sample change over time. Your role: You will develop and implement algorithms to find, quantify and track mutations in evolving populations
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. For example, we would like to be able to track how the prevalences of different strains in a mixed sample change over time. Your role: You will develop and implement algorithms to find, quantify and track
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description Cities depend on sufficient and sufficiently clean water. However, we often lack the data to fully understand the dynamics of contaminants throughout the urban water cycle. Existing sensors
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, specialized compilers, and robust algorithms and implementations, in addition to the creation of dedicated design tools and methodologies. The Computer Engineering (CE) section of the Department of Quantum
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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