123 advance-soil-structure-modeling Postdoctoral positions at MOHAMMED VI POLYTECHNIC UNIVERSITY
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. The successful candidate will develop advanced machine learning (ML) models to automate and optimize retrosynthetic analysis, facilitating the discovery of efficient and sustainable synthetic routes for complex
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Technology Institute (GTI) at UM6P leads cutting-edge work in: Renewable energy systems (solar, wind, smart grids) Advanced digital technologies (Industry 4.0, IoT, automation) Green process engineering
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phosphate industry Experience in chemical process engineering Knowledge in advanced structural characterizations will be valued. Familiar with risk assessment and safe working procedures in a chemical
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at advancing sustainable chemical processes, including but not restricted to carbon dioxide (CO₂) conversion, volatile organic compound (VOC) abatement, and hydrogen production/ storage…... The position requires
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exploration and characterization of igneous systems. The successful candidate will join a dynamic research group focused on advancing our understanding of carbonatite geology, mineralization processes, and
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Science, Energy Processes or another suitable engineering discipline. Sound background in Energy systems and Fuel cell hydrogen applications. Skills in modeling & structural analysis using various existing
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(especially libraries like Pandas, NumPy, SciPy, GeoPandas, etc.), and R. Advanced skills in predictive modeling and machine learning, particularly for multi-variable simulations. Knowledge of complex systems
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project focused on the sustainable valorization of phosphate-rich mining waste through innovative comminution strategies. The successful candidate will play a leading role in advancing research
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an innovative approach, UM6P places research and innovation at the heart of its educational project as a driving force of a business model. In its research approach, the UM6P promotes transdisciplinary
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of mining processes, mathematical modeling of flows and extraction decisions, and the use of machine learning algorithms to predict ore quality and optimize operational decisions. 2. Key Responsibilities