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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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. This project addresses that challenge through chip-level implementations of SSM architectures in advanced CMOS nodes, targeting latency- and energy-constrained environments. Research area and project description
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the interfacial phenomena between water contaminants and adsorbent materials. As a member of the “Nano-Micro-Macro. Structure in Materials” research group, led by Prof. Joerg Jinschek, you will push the boundaries
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mentoring by experts in analog and neuromorphic electronics Opportunities for international collaboration, travel, and research dissemination Join us in creating the next generation of intelligent, low-power
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to join a cutting-edge research project at the intersection of microelectronics and quantum computing. As the quantum computing field rapidly advances toward large-scale, fault-tolerant systems, one
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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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dynamic and international research environment. Our research facilities include top-notch optics laboratories and access to a world-class cleanroom. Principal supervisor is Prof. Albert Schliesser (email