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contribute to developing new theoretical, historical, and practice-based insights on how heritage preservation and urban development interact in creating (or addressing) spatial inequalities in African and
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multidimensional networks by studying the theoretical foundations behind randomized algorithms in the context of sparse optimization and applications in real world data sets. We are also interested in exploring
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contribute to developing new theoretical, historical, and practice-based insights on how heritage preservation and urban development interact in creating (or addressing) spatial inequalities in African and
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are seeking a highly motivated candidate to contribute to the development of new catalytic systems through computational simulations and theoretical studies. Main Responsibilities: Perform computational
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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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into sparse optimization frameworks for the purpose of completing multidimensional networks by studying the theoretical foundations behind randomized algorithms in the context of sparse optimization and
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. The candidate must hold a PhD in Urban or Rural Development, Civil Engineering or related domain. The candidate is expected to have hands-on experience in field related to urban or rural planning, renewable
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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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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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using advanced geological modeling tools such as Datamine software, Leapfrog Geo and Surfer. Supervise MSc/PhD students working on related tasks. Publish high-impact papers and contribute to funding