114 embedded-system-"https:"-"https:"-"https:"-"https:"-"CIPMM---Systemic-Neurophysiology" positions in Morocco
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Job description: We are looking for a postdoctoral researcher specialized in electronics and embedded
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Job description: We are looking for a postdoctoral researcher specialized in electronics and embedded systems, capable of designing and deploying innovative IoT solutions for research projects
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Postdoctoral Researcher (PostDoc) in Blue Economy, Coastal Luxury Tourism, Yachting Industry & Territorial Development Position Overview This position is launched by the Sustainable Real Estate
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Postdoctoral Researcher (PostDoc) in Coastal Design, Blue Economy Innovation, Luxury Tourism & Yachting Development Position Overview The Sustainable Real Estate Innovation Lab (SuRE In Lab
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About UM6P: Located at the heart of the future Green City of Benguerir, Mohammed VI Polytechnic University (UM6P), a higher education institution with an international standard, is established
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About Mohammed VI Polytechnic University (UM6P) Mohammed VI Polytechnic University (UM6P) is an internationally oriented institution of higher learning, that is committed to an educational system
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with sustainability frameworks (blue economy, circular economy, ESG, regenerative coastal development). Ability to produce actionable, policy-oriented insights. Where to apply Website https
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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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22 Jan 2026 Job Information Organisation/Company MOHAMMED VI POLYTECHNIC UNIVERSITY Research Field Architecture Environmental science Geosciences Researcher Profile Recognised Researcher (R2
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critical thinking and academic excellence. A crucial aspect of this position is embedding ESG concepts into business, economics, and policy curricula, creating an interdisciplinary and dynamic learning