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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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the same engine we can use the best features of them. Done right, the AI controlled engine can even be totally fuel agnostic, being capable of using any sustainable fuel without hardware modification. The AI
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section Energy Technology and Computer Science, where you will have around 20 colleagues with a mix of research and industrial experience. We work with research, innovation, technology implementation, 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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engineering, or a closely related field. Knowledge on SSM algorithms, Hands-on experience with layout and tape-out of CMOS chips. Experience in AI chip prototyping and hardware-software co-design. Solid
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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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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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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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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