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mining waste deposits (MATs). This project integrates mineralogical and mechanical characterization, pilot-scale testing, and advanced process simulation, with the objective of optimizing grinding
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dedicated to advancements in sustainable energy through innovative research and training. Our focus lies in the design, development, and optimization of inorganic materials for various sustainable energy
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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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embedded systems and hardware engineering teams to integrate AI models into the BMS. Optimize AI/ML pipelines for resource-constrained environments, including edge AI applications. Guide PhD students and
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, including experts in mineral engineering, chemistry, and process optimization. Supervise MSc/PhD students working on related tasks. Publish high-impact papers and contribute to funding applications
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coatings, to protect the LFP particles while optimizing their performance in high-energy-density applications. The coated LFP materials should exhibit superior mechanical and chemical resilience, ensuring
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Postdoctoral Researcher positions to support its growing research portfolio in renewable energy-driven water production and condensation-based system optimization. The positions are designed for early-career
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multiplex and multilayer networks alongside with the observed links in order to predict or reconstruct the missing links. The first step is to explore different optimization methods using low rank tensor
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, the validation is monitored through design of experiments and optimization of workflows much more rapidly than conventional approaches. The HTMR is a 100% Applied Sciences research laboratory, and is setting up
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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