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Field
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standard, was established to serve Morocco and the African continent and to advance applied research and innovation. This unique university, with state-of-the-art infrastructure, has woven an extensive
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projects. The primary project at CRISES involves designing, implementing, and analyzing nationally representative surveys that assess attitudinal and behavioral support for achieving a genuine democracy for
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focusses on axon-glia interactions and on how glial metabolism regulates axon integrity and myelination (e.g., Nature Neuroscience, PMID 32807950; Nature Neuroscience, PMID 25195104; PNAS, PMID 28484008
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, this position offers a unique opportunity. You will work with real robots, real sensors, and real physical interaction problems, contributing directly to the development of autonomous mining systems capable
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interdisciplinary scientific experience. Our approach integrates cutting-edge tools and technology, empowering researchers to push the limits of knowledge and innovation. With us, your curiosity will know no bounds
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facilities, sustained funding, and collaborative environment. You will design and execute experiments, contribute to high-impact publications, and play a key role in the training and mentorship of junior
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. This exciting project focuses on further development of beam position monitoring structures and high gradient testing of components. They will play a key role in designing, fabricating, and testing advanced
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publications. You will work alongside PhD students and interact with experimental partners across the NAP4DIVE consortium. You will have access to the DelftBlue high-performance computing cluster. This position
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have a strong background in cellular and molecular biology, malaria parasitology, demonstrated success as a recent PhD graduate, creative out-of-the box thinking, and willingness to join and work with a
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training pipelines using modern ML frameworks Generating data on miBd–pMHC interactions to guide iterative model optimization, espeicially for specificity Benchmarking AI-designed recognition modules against