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. Development of real-time optimization algorithms and model predictive control (MPC) strategies for adaptive process management. Addressing data sparsity and data quality issues in industrial process data
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. We propose the integration of randomized algorithms into sparse optimization frameworks for the purpose of completing multidimensional networks by studying the theoretical foundations behind randomized
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join our research team focused on the development of innovative and sustainable sensor systems for industrial applications. This position offers an exciting opportunity to contribute to cutting-edge
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. 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 experts to integrate LLM-based
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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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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), overall health
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algorithms in various projects. Contribute to the supervision of doctoral students and interns at the center. Participate in the training courses organized by the center. Work on collaborative projects with
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computing, and digital twin technologies for mission autonomy and predictive analytics. The research assistant will develop real-time flight control algorithms, AI- based data processing, and mission
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of precipitation and temperature over Morocco. Key Responsibilities: Statistical model development: Led the development of advanced statistical models and machine learning algorithms for forecasting precipitation
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environmental sensors and thermal cameras (UAV/IR). Comparative analysis of PV configurations: with vs. without vegetation. Studying dust deposition and its impact on energy performance. Analyzing meteorological