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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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recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically LLMs
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coefficient modeling. Experience in integrating modeling with leaching and separation processes for scale-up. Advanced data interpretation using electron diffraction, dendritic growth modeling, and REE behavior
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. Knowledge of cloud-based computing platforms for data processing (e.g., AWS, Google Cloud). Understanding of BMS architecture and electric mobility systems.
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Mathematics, Data Science, or related fields. Strong background in Operations Research. Experience with industrial data processing, particularly in dynamic, real-time environments. Proficiency in machine
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journals in the field. Participate to the supervision of PhD students and research internships. Criteria of the candidate: PhD in the field of Cryptography, Computer security or any related field. Strong
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, opportunities for conference participation. Application Procedure Application must include: A detailed CV A cover letter highlighting relevant experience A list of publications Contact information for 1–2
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have expertise in geometallurgy, applied geochemistry, and geo-environmental assessment, with advanced proficiency in integrating spatial data into 3D models using sophisticated interpolation techniques
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Institute (GTI) at UM6P specializes in: Renewable energy systems (solar, wind, smart grids) Advanced digital technologies (Industry 4.0, IoT, automation) Green process engineering (circular-economy and low
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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