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for intelligent brain-computer interfaces? We are offering a PhD position in analog/mixed-signal CMOS circuit design for EEG and wearable sensor interfaces, as part of a pioneering project focused on assistive
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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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to optimize performance and interpretability, analogous to RAG (Retrieval-Augmented Generation) in LLMs Investigating multiple models for analysis, focusing on the Occam’s Razor principle of preferring simpler
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-design SSM-inspired spiking neural network (SNN) cores and integrate them with a low-power RISC-V processor. The PhD candidate will develop spike-based computing blocks and explore hybrid analog/digital
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-out key analog and mixed-signal building blocks (e.g., low-noise amplifiers, voltage/current sources, multiplexers, digital logic) for operation at cryogenic temperatures. Develop measurement setups