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research at the intersection of software-defined radio, edge AI, and real-time spectrum sensing, with a core focus on implementing and deploying AI-driven RF sensing and anomaly detection systems on resource
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strong knowledge in at least two of the following research directions: statistical signal processing, e.g., detection theory and estimation theory; signal compression & sparse representations, e.g
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RF/test & measurement equipment. It is desirable that you have strong knowledge in at least two of the following research directions: RF hardware integration, e.g., mixers, amplifiers, SDR, FPGAs
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machine learning methods for detecting, classifying, and identifying wireless anomalies in real-world radio environments. You will design and experiment with AI-driven approaches for spectrum analysis, work
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desirable that the candidate has a strong knowledge in at least one of the following research directions: array signal processing Radio channel sounding, estimation, and modelling Integration design of SDR
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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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++) Knowledge of the fundamentals of ML/AI algorithms for communications and networking, and their implementation A creative mindset and curiosity to research and develop new solutions with highly skilled
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communication and sensing satellite and non-terrestrial networking; interplay between AI/ML learning/inference and communications; experimental works on open-radio access network (O-RAN). The group features a