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of UAV demonstrators at UM6P and OCP sites. Methodology: AI Development: Reinforcement learning, computer vision, and sensor fusion for autonomy. Digital Twin & Edge Computing: Implement real-time
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and real-time data acquisition systems. Participate in testing and deployment of UAV demonstrators at UM6P and OCP sites. Methodology: AI Development: Reinforcement learning, computer vision, and sensor
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to analyze and interpret unstructured data (e.g., maintenance logs, sensor data, technical reports) for predictive insights Collaborate with domain experts to integrate LLM-based solutions into predictive
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) to predictive maintenance challenges. Develop and fine-tune LLMs to analyze and interpret unstructured data (e.g., maintenance logs, sensor data, technical reports) for predictive insights Collaborate with domain
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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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such as air quality monitoring, water leak detection and energy monitoring of electric vehicle batteries. The candidate will need to master sensor technologies, embedded systems, and communication protocols
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and sustainable sensor systems for industrial applications. This position offers an exciting opportunity to contribute to cutting-edge research in the field of sustainable materials to develop promising
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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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stress. Responsibilities: Monitoring PV panels: temperature, efficiency, dust accumulation. Using environmental sensors and thermal cameras (UAV/IR). Comparative analysis of PV configurations: with vs