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- NTNU Norwegian University of Science and Technology
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defined radios ▪ Prior knowledge of wireless communication systems (MIMO-OFDM, LTE, 5G-NR) ▪ Prior knowledge of information theory Our offer ▪ Access to state‑of‑the‑art lab facilities at the TUM ACES Lab
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-level languages (Python, Matlab) Affinity with hands-on experiments is desired General knowledge in physics, particle accelerators and radio frequency systems is a plus Fluent in English Open to team work
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architectures capable of operating in changing environments and supporting future cognitive radio systems. This project aligns with global research priorities in next-generation communications, energy-efficient
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, networking, and signal processing. You have at least intermediate knowledge of machine learning algorithms. Knowledge of satellite communications, wireless sensing, radio propagation, optimization, or digital
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of analog and Radio Frequency (RF) Integrated Circuits (IC). The group's motto is “fundamental solutions for practical problems” and under this vision, it has contributed many fundamental new ideas in circuit
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position Are you motivated to take a step towards a doctorate and open
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website about knowledge security . Please do not contact us for unsolicited services. Where to apply Website https://jobmarketingstats.nl/TUDelft/Redirect/eZ_lfMdOeXOAUQc_Ygumog== Requirements Research
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networks should no longer force users to adapt to the network. Instead, the network should continuously adapt itself to the users, intelligently orchestrating radio-frequency (RF) and optical wireless (OWC
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of Chichester are pleased to announce the availability of a fully funded Collaborative doctoral studentship from October 2026 under the AHRC’s https://www.ahrc-cdp.org/2025-ahrc-collaborative-doctoral
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AI systems for real-time 3D mapping on compact, low-power devices. The project will combine optical sensing, event-based vision, and radio-frequency (RF) data with advanced AI to build robust mapping