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computational framework, integrated with deep reinforcement learning (DRL) methodologies for both gene-level and edge-level perturbation control, represents a significant advancement in the computational toolkit
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computational modeling and/or analysis of complex biological systems, integrating state of the art tools such as machine and deep learning approaches. Experience in managing biological databases and statistical
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, clustering, classification • deep learning, variational auto-encoding, back-propagation • excellent knowledge of R and Python. The knowledge of C++ would be a plus. Application: Application files should
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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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the understanding of deep latent variables models for unsupervised learning with massive and evolving heterogenous data. Although deep learning methods and their statistical extensions, the deep latent
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rank models [Sportisse et al., 2020], random forests [Stekhoven and Buhlmann, 2012] or deep learning techniques with variational autoencoders [Mattei and Frellsen, 2019, Ipsen et al., 2021]. One
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from one round to the next, and eventually the library collapses to a few selected functional aptamers. The evolution can be tracked in detail by deep sequencing of the successive rounds. The goal
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Artificial Intelligence (applied mathematics, computer science, etc.), or a thesis defense scheduled for 2025. • Research contributions in deep learning, statistical learning, natural language processing (NLP
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of specialized deep learning models (neural network or transformer) for automated segmentation of tibial plateau fractures. iii) The algorithm must then be trained to allow it to learn the morphologies of bone
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updated with the latest advancements in AI and deep learning methodologies, ensuring cutting-edge technical standards. Methodological Development: Develop customed deep learning architecture and/or