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, copyrighted, or biased. By studying brain data recordings and building computational models that mimic real populations of neurons, the project aims to uncover active unlearning: how the brain learns
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well as computational modeling. The development and numerical implementation of novel methods has become a key issue in modern oncology, both in terms of understanding the biology of cancers and for medical oncology
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to perform and analyze experiments involving neonatal mouse models, behavioral testing, and brain tissue analysis. Responsibilities: Assist in the design and execution of in vivo experiments using mouse models
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into biologically relevant hypotheses about the physiological and pathological mechanisms of mental processes. Responsibilities: Develop and implement relevant computational models relative to specific behaviors and
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Post-doctoral Position in AI Causal models for Synchrotron Anomaly Detection H/F This post-doctoral position is part of a collaboration between LIAD (Laboratory of Artificial Intelligence and Data
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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establishments (Université Côte d'Azur, CNRS, INRAE, INSERM ...), but also with the regiona economic players. With a presence in the fields of computational neuroscience and biology, data science and modeling
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group: MARIANNE (https://team.inria.fr/marianne/). The MARIANNE project-team pursues high-impact research in Artificial Intelligence with a focus on data and models for computational argumentation in
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, a computational approach with the generation of realistic models of the auditory system allows us to make theoretical predictions and verify our experimental results. The engineer will be responsible
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. In addition to being able to compute both the Wasserstein distance and the transport map, our method outperforms model-free methods, in high dimension, even in the case of non-Gaussian distributions