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experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
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uncertainties (delays, resources, failures) using various methods, including Bayesian approaches. 3. Optimize the workshop configuration, taking into account scenario variability, by relying on the surrogate
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inference by constraining all operations, including matrix multiplications and activations, to integer arithmetic. This line of research has already led to promising results in semantic segmentation with
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), computation takes place only when an event occurs. Beyond reducing the amount of incoming data to process, event-based computing requires fewer operations per second during the inference phase compared
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statistical inference. More specifically the recruited PhD student will consider variational inference approaches for GEA and stochastic optimization to speed up the inference, with the objective of scaling up
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associated with phenotypic (biomechanical and metabolomics) traits. Estimate locus-specific effect sizes and quantifying genetically-driven phenotypic variations. Develop Bayesian models and/or deep learning
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background in one of the following areas: Statistical Physics Applied Mathematics Statistics & Bayesian Inference Proficiency in Python is also expected. Contacts dbc-epi-recrutement at pasteur dot fr
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behaviour using computational approaches such as Bayesian program synthesis and inverse reinforcement learning. Investigate the diversity of motor commands that could implement observed behaviours and explore
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 18 days ago
, 3], significant challenges related to running complex AI algorithms such as Deep Neural Network (DNN) inference on lightweight platforms with limited computational power and relying on potentially
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diagnosis6 . Objectives: The goal of the internship is to conduct pilot analyses to investigate the potential of machine learning approaches to infer latent neuroimaging phenotypes displaying maximum fit with