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10 Feb 2026 Job Information Organisation/Company MOHAMMED VI POLYTECHNIC UNIVERSITY Research Field Computer science Engineering Engineering Researcher Profile Recognised Researcher (R2) Established
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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
computational chemistry techniques and data-driven approaches to optimize the properties of novel polymer-based materials. Key duties The successful candidate is expected to: Build and evaluate chemical databases
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Mohammed VI Polytechnic University (UM6P) - Chemical & Biochemical Sciences CBS Department Qualification Type: Postdoctoral position Location: Ben Guérir, Morocco Funding for: National/International
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10 Feb 2026 Job Information Organisation/Company MOHAMMED VI POLYTECHNIC UNIVERSITY Research Field Computer science Researcher Profile Recognised Researcher (R2) Established Researcher (R3
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10 Feb 2026 Job Information Organisation/Company MOHAMMED VI POLYTECHNIC UNIVERSITY Research Field Computer science Researcher Profile Recognised Researcher (R2) Established Researcher (R3
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Profile: Required qualifications : PhD in mineral processing, materials science, metallurgical engineering, or related discipline; Proven experience in ore characterization and process development; Strong
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(SusMaT-RC) at the University Mohammed 6 Polytechnic (UM6P) and Massachusetts Institute of Technology (MIT). The PDF will be enrolled at SusMaT-RC under the supervision of Professor Youssef Habibi. Our
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Center on Air Quality and Climate (ARC_AIR) is a research unit within UM6P dedicated to advancing scientific understanding and innovative solutions in the fields of air quality, atmospheric chemistry, and
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10 Feb 2026 Job Information Organisation/Company MOHAMMED VI POLYTECHNIC UNIVERSITY Research Field Computer science Researcher Profile Recognised Researcher (R2) Established Researcher (R3
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chemistry models, and physics-informed deep learning, validated through field campaigns. Key Responsibilities Conduct atmospheric chemistry simulations Develop deep learning models for emission predictions