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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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analogous neural circuitry and shared molecular pathways have established songbirds as the model system of choice for human speech learning and fine motor control in general. The PhD candidate will use
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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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-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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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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-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