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Field
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combs and their applications in neuromorphic computing and high-speed optical fibre communications. You will also be involved in the design, development and numerical modelling of novel signal processing
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for spatially resolved single- and sub-cellular omics, and the Smart Trials Hub, which is pioneering patient-centric sampling and digital health integration. This is a full time post (35 hours per week), and you
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hub for spatially resolved single- and sub-cellular omics, and the Smart Trials Hub, which is pioneering patient-centric sampling and digital health integration. We’re looking for someone with strong
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of Research ExperienceNone Additional Information Eligibility criteria - PhD in Medical Imaging, Biomedical Engineering, Signal Processing, or a related field. - Strong expertise in MRI physics, preferably in
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participant outcomes. The project will use a variety of approaches, including human perceptual experiments, machine learning, digital signal processing, and computational models of hearing. UConn has a vibrant
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are those defined in point 4. For the purpose of determining the appropriate profile for the activity to be developed, the following are special requirements: a) proven experience in the scientific area of
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-structure interaction of the Maeslant Barrier, combining field modal testing, numerical modelling, and signal processing to capture and interpret its dynamic response under current and future hydraulic and
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Trials Hub, which is pioneering patient-centric sampling and digital health integration. This is a full time post (35 hours per week), and you will be offered a fixed term contract for 24 months. Research
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: Strong background in communication theory, signal processing, and wireless communications Proven track record of publications in relevant IEEE journals and conferences Extensive experience in physical
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Autonomous Transportation. As far as technical enablers are concerned, we leverage expertise on advanced technologies including semantic/task-oriented data processing, signal processing, network resource