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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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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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activity recording datasets investigating fear memory extinction in rodents [5-11]. 1.3. Develop a consistent framework to integrate the heterogeneous data (e.g., fiber photometry, calcium 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
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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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Pasteur/INSERM, Paris, France), Mark Brönstrup (chemistry, Helmholtz Centre for Infection Research, Braunschweig, Germany) and Christophe Zimmer (computational biology, University of Würzburg, Germany and