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Field
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spacecraft data management; monitoring and control systems, including for pre-launch, and testing procedures; electrical ground support equipment and system checkout equipment. In the context of digital twin
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movement, using state-of-the-art MRI methods alongside simultaneous physiological monitoring. The Postdoctoral Associate should possess good verbal and written communication skills in English, the ability
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: autonomous and fault-tolerant systems (including health monitoring systems), advanced guidance, control, estimation and optimisation techniques and tools, and the technology development of GNC sensors with a
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to software and/or hardware development and to calibration and data quality monitoring. • Review and discuss research plans and details with other members of the research group and colleagues, and with
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
the Histocompatibility Laboratory capacity. In this role, the Postdoctoral Research Associate will provide oversight of clinical testing in the Histocompatibility Laboratory. Oversight includes ongoing monitoring
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focus on autoimmune disorders and related conditions. The associate will independently execute studies, analyze data, and contribute to publications, while collaborating with a multidisciplinary team
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For detailed information on benefits and eligibility, please visit: http://uhr.rutgers.edu/benefits/benefits-overview . Position Status Full Time Working Hours (per week) Standard Hours 37.50 Daily Work Shift
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absorption. In particular, its estimation is essential for monitoring the vegetation's health and water status, and thus making decisions on irrigation scheduling and optimal application rates that, on one
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Monitoring (LTVEM) in the hospital for management and diagnosis of epilepsy. The technology is built on brain computer interfaces equipped with a Spiking Neural Network (SNN) and aims at early detection
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from real world longitudinal data on management and health outcomes for children with mental health conditions. Methods have included deep learning, large language models (LLM), generative AI models (Gen