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and systems based on III-V and CMOS process technologies for signal amplification, conditioning, sampling, and wireless data transfer for sensing systems that operate in harsh environmental conditions
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Doctoral student in Electrical Engineering focusing on design of radio frequency integrated circuits
research is to be performed in close cooperation with colleagues at both Lund University and Linköping University. The research area for the position is radio frequency integrated circuits for wireless
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-nitride devices and circuits. Our research in GaN HEMT technology target application within wireless communication and radar systems. About the research project This PhD student position will focus
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works within wireless communications in a wider sense. The activities range from communication theory, information theory, joint communication and control, channel measurements/modeling, development
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Join us in shaping the future of wireless communication by developing a fundamentally new transmitter architecture that pushes the limits of energy efficiency and data capacity. About us The
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education in digitalization and electrification including renewable energy sources, electric vehicles, industrial IoT, AI, 6G communication and wireless sensor networks as well as research and education
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intelligence, providing significant contributions to the broader fields of wireless communications, IoT, and beyond. You will actively develop and gain hands-on experience with high-performance AI modeling
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within the Department of Electrical and Information Technology works broadly with research within Cryptography, Computer Security, Wireless and Fixed Networks. The security group within the Division has
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in spectral monitoring and signal intelligence, providing significant contributions to the broader fields of wireless communications, IoT, and beyond. You will actively develop and gain hands
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distributed wireless systems" which is conducted in collaboration between Linköping University (LiU) and Lund University (LU). Read more here: https://elliit.se/project/machine-learning-for-sensing-in