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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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for the European Higher Education Area ) in pharmacy, chemistry or a similar subject and a minimum of 4 years’ professional experience in a similar role (e.g. developing and/or maintaining stakeholder engagement
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knowledge of the angular degrees of freedom the direct connectivity, we have shown that missing connections can be predicted reliably [Z]. Second, we have developed novel sampling strategies in torsion angle
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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
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mechanoelectrical feedback, it requires specific tools to uncouple them and to decode the transformation of complex acoustic stimuli by the brain. In the lab, we are developing electrophysiological and imaging
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The Machine Learning for Integrative Genomics team at Institut Pasteur, headed by Laura Cantini, works at the interface of machine learning and biology, developing innovative machine learning
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-mediated inflammation is a key early event in the development of tumor-associated inflammation—a hallmark of cancer that drives tumor growth and progression. AIM2 expression is notably elevated in lung
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data. 2 Postdoc Subject The main goal of this postdoc is to develop open-world 3D scene understanding models through the fusion of LiDAR-based models and VLM. This goal can be achieved by solving
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focused on deep-phenotyping of individuals with autism and controls including brain imaging (MRI, fMRI, DTI and EEG) and a battery of cognitive tests. Our group is currently developing new methods