9 health-monitoring Postdoctoral positions at MOHAMMED VI POLYTECHNIC UNIVERSITY in Morocco
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) to monitor and improve public health. You will contribute to modeling smart cities with a focus on health and designing decision-support tools for sustainable and inclusive urban environments. Key
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for real-time battery health monitoring and fault detection. Collaborate with embedded systems and hardware engineering teams to integrate AI models into the BMS. Optimize AI/ML pipelines for resource
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develop innovative solutions based on the analysis of urban data (big data, IoT, GIS) to monitor and improve public health. You will contribute to modeling smart cities with a focus on health and designing
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), overall health, and more. For instance, in electric vehicles, the BMS carries out functions such as monitoring, controlling, optimizing, and ensuring safety to mitigate potential hazards related to battery
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. Analyze large datasets from battery systems to improve model accuracy and performance. Conduct research on predictive analytics for real-time battery health monitoring and fault detection. Collaborate with
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GSMI - Postdoctoral Researcher - Plant Ecology and Soil-Plant Interactions in Vegetated Photovoltaic
communication of results. Ensure strict adherence to health, safety, and environmental protection regulations in accordance with UM6P standards. Candidate Profile: PhD in plant ecology, environmental sciences
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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
, and Precision Health. The project aims to leverage AI and machine learning (ML) to analyze complex metabolomics datasets and address key health challenges, including biomarker discovery, disease
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covert, soil health and quality, soil health indictors, sustainable agriculture, field experiments monitoring. About UM6P: At the heart of the future Green City of Benguerir, Mohammed VI Polytechnic
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absorption. In particular, its estimation is essential for monitoring the vegetation's health and water status, and thus making decisions on irrigation scheduling and optimal application rates that, on one