432 machine-learning-"https:" "https:" "https:" "UCL" Postdoctoral positions in France
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, France [map ] Subject Areas: Machine Learning / Machine Learning Mathematics Statistics Statistical Physics Probability Appl Deadline: 2025/12/20 11:59PM (posted 2025/11/25, listed until 2026/05/25
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: EDPs2 (Partial Differential Equations: Deterministic and Probabilistic Studies), Geometry, and LIMD (Computer Logic and Discrete Mathematics). This diversity of research topics within a single laboratory
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of Learning and Development (LEAD- UMR-5022), at the Université Bourgogne Europe, CNRS (https://lead.ube.fr/ ) . To apply, please submit: - CV - Cover letter describing your interest in the position
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TAGADA started on Oct, 1st 2025 https://www.ceremade.dauphine.fr/dokuwiki/anr-tagada:start . The goal of TAGADA is to lay the foundations of random tensor theory and foster a multidisciplinary research
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self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed to irrigate
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-machines and nano-robots as a drug delivery vehicles. As a core research activities, the postdoc candidate activities are the: - Design and in silico modeling of DNA origami nanostructures with high
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 19 days ago
hyperscanning neuroimaging data, using advanced statistics and machine learning methodologies for temporally-sensitive data, such as GLMM, Random Forests, LSTM, etc.. Use of MatLab for pre-processing, and
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(Partial Differential Equations: Deterministic and Probabilistic Studies), Geometry, and LIMD (Computer Logic and Discrete Mathematics). This diversity of research topics within a single laboratory reflects
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researchers with ample experience in MEG/EEG data analysis, BCIs, signal processing, deep learning for brain imaging analysis, biomedical statistics, dynamical systems and research on motor control. The lab has
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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a