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closely with signal processing pipelines built on real measurement data — including baseband I/Q signals — and contribute to both algorithm development and experimental validation. The role involves close
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algorithms into efficient edge inference systems, and validate end-to-end performance under real-world deployment conditions. Throughout, you will work with live measurement data spanning the full arc from
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. vision–language and multimodal models) often perform well during training but degrade after deployment due to changes in data, environment, sensors, or user behaviour. The goal of this PhD is to design
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-loss events undermine statistical confidence. The aim is to develop i) edge intelligence (on-turbine smart algorithms for data preprocessing), ii) resilient data movement (error-tolerant, cybersecure
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systems, including integration of cyber-security. Enhance indoor climate by defining control strategies and developing control algorithms for demand-control ventilation, heating and cooling systems
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to data from various sensors and radio signals? This is the main underlying theme to be explored within this postdoctoral position. The appointed researcher will investigate how AI embedded in physical
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. The primary responsibilities and tasks are: Conduct research in robust positioning methods utilizing GNSS with emphasis on signal- and data processing algorithms and fusion with other sensors with emphasis