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Field
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are responsible for blood and immune cell production during development. We will now establish how these transient embryonic progenitors and their progeny respond to prenatal challenges, convey persistent
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neurons and behavior. However, extracting meaningful insights from extensive and noisy recordings necessitates the development of new, statistically robust methodologies. Recent experimental studies
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train robust machine learning (ML) algorithms without exchanging the actual data. The benefits of such a decentralized technology over personal and confidential data are multiple and already include some
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modeling the dynamic of the data evolution is clearly important. The purpose of this postdoc position, within the Institut 3IA Côte d'Azur (Univ. Côte d’Azur & INRIA), will be focused on the development and
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techniques and the structure of bilevel problems in large-scale settings. Objectives The goal of this postdoctoral project is to develop scalable blackbox optimization algorithms tailored to bilevel problems
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the development of more efficient online learning algorithms for manifold-valued data streams, with an initial focus on change-point detection, opening the door to new unsupervised data exploration methods. Next
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-world recognition setting will be developed to classify changes on-the-fly into either previously seen classes or unknown classes. Applications to smart cities monitoring are considered.
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access to the unobserved values, and therefore, cannot compute this error. The goal of this postdoc will be to develop a direct method, based on self- supervised learning. The closest related works are two
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Description of the offer : We offer 2 postdoctoral positions dealing with the development of intermetallic materials for permanent magnets and magnetocaloric applications. Position 1: “Designing
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), Nix (https://nixos.org) and Spack (https://spack.io). In direct contact with the development teams of these tools, with the supercomputer administration teams, and with our foreign counterparts