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is driven by the increasing introduction of renewable energy sources (RES), both in large-scale installations (wind farms, solar) as well as distributed over large number of small-scale assets (e.g
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-intensive, often being non-interpretable, and being highly brittle towards shifts in data distribution and changes in the problem domain. This fully-funded three-year project, with the possible further
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that develops advanced AI compute solutions involving AI models, algorithms, implementations, sensors and hardware for small scale edge up to large scale distributed and hybrid hardware architectures
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-scaled CMOS models. Advanced algorithms and architectures need investigation of power-consumption trade-offs. For instance, digital predistortion (DPD) can enhance power amplifier efficiency but
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of distributed MIMO, and/or coordinated multi-AP operation (under study in the Wi-Fi 8 standardisation workgroup), using Hardware Description Language on FPGA, based on the open-source openwifi project (https
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are searching for a motivated PhD candidate to design practical over-the-air computing algorithms and protocols for future edge AI applications. About the employer The research of this PhD position will be
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in large-scale installations (wind farms, solar) as well as distributed over large number of small-scale assets (e.g., rooftop PV). This calls for an increasing adaptivity, especially in terms