73 parallel-and-distributed-computing-phd-"Meta"-"Meta" positions at Nature Careers in France
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function and differentiation. Through a fully automated high-throughput screening platform, we have identified novel metabolic targets that appear critical for anti-tumor immunity. One PhD project will
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PhD) EUR 3,000 Starting package from EUR 10,000 to EUR 20,000 per year Applicants will be encouraged to apply for complementary external funding (e.g., MSCA or EMBO fellowships) in parallel to/following
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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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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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at various levels (Bachelor, Master, PhD) Provide lectures in Theoretical Computer Science for bachelor and master programs Advise PhD candidates and contribute to doctoral and postdoctoral training (e.g
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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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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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pharmacogenetic variants), creating ancestry-specific imputation panels, calibrated polygenic-score distributions, and incorporating variant calls from a subset of approximately 5000-10000 individuals with long
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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