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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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; proficiency in bioinformatics and R is a strong asset; Training and mentoring junior staff and trainees; Participating in lab meetings and scientific discussions; Contributing to 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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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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, it has matured into an established research community seeking automatic, computerized processing of 3D geometric data obtained through measurements or designs. The following developments have shaped
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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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. Côte d’Azur & INRIA), will be focused on the development and the understanding of deep latent variables models for unsupervised learning with massive heterogenous data. Although deep learning methods and