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Field
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-speed fiber optic telecommunications are now entering the era of multi-core fibers to meet the world’s ever-increasing demand for data transfer. Signal regeneration is a key function of future networks
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. Excellent academic record. Proficiency in Matlab and signal processing. High proficiency in English. Strong motivation to pursue a PhD. Offer Full-time predoctoral research position at UPV. One-year contract
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the field Staying updated with the latest advancements in satellite communication, signal processing, radar technologies, navigation algorithms, and other relevant domains Publishing your research findings in
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with expertise in digital signal processing methods, and machine learning methods for amplitude and phase noise characterization of optical frequency combs, recovery of dual-comb measurement signals and
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CRITERIA (out of 10 points) Only merits related to the specified line of research will be evaluated. - Degree in Computer Engineering, Telecommunications Engineering, or equivalent (2 points). - Doctorate
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student will become part of a team at DTU with expertise in digital signal processing methods, and machine learning methods for amplitude and phase noise characterization of optical frequency combs
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at leading international conferences and publish in top-tier journals. The successful candidate will gain advanced expertise in multi-sensor fusion, signal processing, machine learning, and positioning
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FieldComputer scienceEducation LevelMaster Degree or equivalent Skills/Qualifications Master’s (or equivalent) degree in Telecommunication Engineering or Computer Science. Good knowledge of signal processing and
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signal strength can be observed at different locations. Co-located Wi-Fi access points sharing the same spectrum compete against each other rather than collaborate to serve users. In this PhD position, you
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research group, which leads pioneering work in multi-sensor navigation, signal processing, and system integrity for aerospace, defence, and autonomous systems. The research will deliver a comprehensive