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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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chemical–biological pathway. The advertised positions will support these efforts through research on advanced carbon capture methods, process modelling and optimization, and biological CO2 valorization in
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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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bottlenecks and optimization strategies. The digital twin will serve as a testbed for evaluating engineering trade-offs and guiding future hardware development. The appointed researcher will collaborate with
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collaboration with our clinical colleagues for investigating visual motion in a group of healthy subjects. The successful applicant holds a PhD degree (or equivalent) in a relevant academic area such as applied
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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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are gaining significant interest for sustainable seawater desalination. This postdoctoral position is part of a research project aiming to design, optimize, and evaluate electrochemical systems [electrodialysis
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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