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València announcing a selection process for an indefinite employment contract within the framework of a Line of Research: R&D line: Tuna quality determination using AI at the Edge sensors https://www.upv.es
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University of North Carolina Wilmington | Wilmington, North Carolina | United States | about 14 hours ago
the University of North Carolina System since 1969. Department School of Health & Applied Human Sciences - 31530 External Link to Posting https://jobs.uncw.edu/postings/39159 Vacancy Number TSP5644PST Working
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biomarkers and their integration with nanoplasmonic sensors to develop next-generation, non-invasive platforms for disease detection and longitudinal monitoring. The Role Led by Prof. Dan Lambert and a
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sensores y las interfaces analógico-digitales Motivación para trabajar en equipos interdisciplinares en los ámbitos analógico, digital y de la IA Contribuir al diseño y desarrollo de arquitecturas de señal
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programme including 16 PhD students trained in several European countries (https://cordis.europa.eu/project/id/101227338 ). You will be appointed for three years, during which time you will study for a fee
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multimodal AI algorithms for fire, smoke, and hot-work detection by fusing optical, thermal/infrared, LiDAR, RADAR, and gas sensor data under varying environmental conditions. Design computer vision and human
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collating and managing data with version-controlled programmatic workflows in R and/or Python, including large volumes of high-frequency sensor data and model outputs; working with investigators to design and
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Multi-Sensor Data Fusion and Smart Decision Making School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Lyudmila Mihaylova Application Deadline: Applications
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published. Website for additional job details https://www.cd6.upc.edu/ Work Location(s) Number of offers available1Company/InstituteCentre for Sensors, Instruments and Systems Development (CD6
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:2405.08111, 2024. https://doi.org/10.48550/arXiv.2405.08111 [4] R. Ketfi, Z. Al Masry, N. Zerhouni, C. Devalland, “MS-DQI: A methodology for data quality assessment in medical sensor networks with a case study