73 parallel-and-distributed-computing-"LIST"-"Humboldt-Stiftung-Foundation" positions at Nature Careers in France
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Leveraging the spatio-temporal coherence of distributed fiber optic sensing data with Machine Learning methods on Riemannian manifolds Apply by sending an email directly to the supervisor
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of programming, learning theory, parallel algorithms or quantum computing Research publications in theoretical computer science conferences and journals Experience in teaching Computer Science topics
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Nature Careers | Port Saint Louis du Rhone, Provence Alpes Cote d Azur | France | about 2 months ago
protein expression and purification, capable of producing thousands of proteins in parallel within weeks . 2) Eukaryotic expression systems facility for production of challenging protein targets. 3) A fully
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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, dynamic and innovative researcher to integrate our community. The ideal candidate will possess deep expertise in the application of cutting edge computational methods to understand the brain mechanisms
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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projected new Master's programme and to teach within the doctoral and lifelong learning programmes of the university For further information about the role, please contact: Prof. Dr. Boris Traue, Director of
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development and The postholder will also have the opportunity to contribute to the construction of a projected new Master's programme and to teach within the doctoral and lifelong learning programmes
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the exact calculation of the square-root and inverse square-root of the source distribution covariance matrix. This approach offers analytical and computational advantages in comparison to existing methods