82 parallel-and-distributed-computing-"U"-"Washington-University-in-St" positions at Nature Careers in France
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Research axis of the 3IA: Axis 3 - AI for Computational Biology and Bio-inspired AI Supervisor (3IA Chair): Emanuele Natale, Sophia Antipolis Laboratory for Computer Science, Signals and Systems
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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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. Processing this response provides estimates of the local variations in acoustic pressure along the fiber, over distances ranging from 40km up to 140km with some systems. This technique, called Distributed
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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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, 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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on the following criteria: • Ability to lead a research program of high risk/high gain projects, supported by a track record of publications and grants. The candidates must meet all criteria to compete for national
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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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The successful candidate will develop computational approaches to discover, model, and develop therapeutic strategies. Examples of potential approaches include: -Network Modeling: Creating
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of several researchers working in the field of inverse problems due to their ability of combining variational inference approaches with the ability of neural networks to learn unknown posterior distributions