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. The system will include: A very compact, ultra-low-power analog front-end (AFE) to sense neural signals. An on-chip neuromorphic processor to convert the neural data into spike-based encoded data and
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applications Strong analytical and problem-solving skills Good communication skills and ability to collaborate across disciplines Motivation to make a real-world impact in sustainable energy and industrial
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Neural Networks (SSM-SNNs). The project includes the co-design and integration of a RISC-V processor for hybrid neuromorphic computing. The research aims to develop ultra-low-power computing chips
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Job Description Are you passionate about designing ultra-low-power electronics for neural and wearable systems? Do you want to develop custom CMOS circuits that serve as the foundation
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-computer interfaces, cognitive rehabilitation, and neural prosthetics. Your contributions will support the development of a custom CMOS-based SNN processor that can operate in ultra-low-power environments
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needed to achieve these temperatures, the control cables connecting to the qubits are often more than 2 meters long. At the same time, a quantum computer powerful enough to solve problems beyond the reach
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architectures. Add some more digital skills Experience with EDA tools such as Cadence Virtuoso, Spectre, or Mentor Graphics. Strong analytical and problem-solving skills, with the ability to work independently