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This project explores the use of state-space models (SSMs) for sequence modeling in large language models (LLMs), focusing on their CMOS-based hardware implementation. The PhD candidate will develop energy
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hardware modification. The AI will learn and adapt the realms of the combustion modes and fine tune the performance for each while the engine is operated. Self-tuning, adaptive, control algorithms will be
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sources. Economic feasibility can be achieved through optimal operational agreements leading to grid resilience, energy security and collective benefits. In this regard, aspects such as regulatory and
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, you will perform benchmarking on materials candidates emerging from CAPeX and collaborative research. Key tasks include: Map out pros and cons of various strategies to upgrade the existing hardware
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Job Description Are you passionate about neuromorphic computing and hardware design? Do you want to contribute to the next generation of brain-inspired computing systems for healthcare applications
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Hardware-software co-simulation and benchmarking This PhD project is part of SDU microelectronic unit’s effort in neuromorphic chip design and collaborates with international partners working on spiking AI
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. The broader goal is to support assistive devices through real-time analysis of brain activity and physical interaction signals using energy-efficient hardware. As a PhD candidate, your primary focus will be
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cryogenic electronics, CMOS behavior at cryogenic temperatures (4 K and below), and quantum hardware architectures (superconducting qubits and spin qubits). Collaborate with quantum physicists to interface