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. The candidate will need to master sensor technologies, embedded systems, and communication protocols, while integrating real-time data into cloud platforms to develop reliable, secure, and scalable solutions
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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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(LLMs) 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
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processes for virtual commissioning and optimization. Examine extensive heterogeneous data sets (including historical process data, sensor data, and laboratory findings) using advanced AI approaches such as
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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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., Teodoro, P. E., ... & da Silva Junior, C. A. (2023). Nutritional monitoring of boron in Eucalyptus spp. in the Brazilian cerrado by multispectral bands of the MSI sensor (Sentinel-2). Remote Sensing
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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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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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unstructured data (e.g., maintenance logs, sensor data, technical reports) for predictive insights Collaborate with domain experts to integrate LLM-based solutions into predictive maintenance workflows. Explore
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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