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Engineering stream. The candidate will also be expected to participate in UM6P's collective projects and external missions, and to provide support to students (PhD and Master), i.e., supervision for the writing
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Polytechnic University aspires to leave its mark nationally, continentally, and globally. About ACER CoE: The centre has been recently created to address enduring process challenges in Chemistry and Engineering
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foundation in catalysis/ separation and chemical/process engineering. The successful candidate will significantly contribute to the conception, synthesis, and evaluation of separation/catalytic systems aimed
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collaboration between Geology and Sustainable Mining Institute (UM6P, Morocco), and Mineral-X (Stanford University, USA). Qualifications PhD in process mineralogy, mineral processing, mining, chemical or
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Management:Assist in managing research projects, including drafting funding proposals, coordinating with partners, and monitoring budgets. Required Qualifications : PhD: A PhD in digital public health, urban health
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applications in linkage with incubation and start-up ecosystems. About the Chemical & Biochemical Sciences Green Process Engineering (CBS) The Chemical & Biochemical Sciences Green Process Engineering Department
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publications Candidate Profile: PhD in catalytic processes or equivalent in a relevant field Relevant experience in conducting catalytic tests Experience in analyzing gas-phase products using chromatographic
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processes. This project focuses on designing and optimizing novel materials that enhance the efficiency and effectiveness of converting biomass into valuable products such as biofuels and fine chemicals. We
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Post-Doctoral Fellow Position: Development of innovative biomass processing for nanofibers extraction 13219 Position Summary: We are seeking a motivated and skilled Postdoctoral Fellow to join our
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. The primary objective is to design robust and efficient planning solutions—integrated within a digital twin—that account for the uncertainties and variability inherent in industrial processes. Machine learning