27 sensor-algorithm-"University-of-California" Postdoctoral research jobs at MOHAMMED VI POLYTECHNIC UNIVERSITY
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component. The battery management system (BMS) unit is not just a component, but a crucial element tasked with monitoring each cell of the battery and running algorithms to calculate state of charge (SoC
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, the next step in this project is to address sparse optimization for tensors. We propose the integration of randomized algorithms into sparse optimization frameworks for the purpose of completing
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conferences and journals. Overview: The successful candidate will join an interdisciplinary team focused on developing innovative numerical algorithms and software to address emerging challenges in scientific
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set of sensors (LiDAR, RGB, IR, and Spectrometer) that enable advanced plant measurements, that allow us to assess plant’s water budget in response to abiotic stress and nutrients’ application
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Responsibilities Design and implement digital twins to monitor and optimize urban systems, including infrastructure, mobility, and energy management. Integrate real-time data from sensors and IoT devices to develop
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, seeks a postdoctoral candidate in wireless sensor networks and the Internet of Things. The successful candidate will join the group of Prof. Youssef Iraqi. Context Wireless sensor networks (WSNs) and
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to join our cutting-edge team, working on the development of advanced AI/ML algorithms for battery management systems (BMS) in electric mobility and micro mobility applications. The primary focus will be
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Responsibilities: Digital Tool Development:Develop digital tools to monitor and analyze the determinants of urban health using sensor data, public databases, and GIS. Predictive Modeling:Design predictive models
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of the extraction and beneficiation system. This work will require an understanding of mining processes, mathematical modeling of flows and extraction decisions, and the use of machine learning algorithms to predict
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, the next step in this project is to address sparse optimization for tensors. We propose the integration of randomized algorithms into sparse optimization frameworks for the purpose of completing