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plasticity platform. Different machine learning strategies will be explored to capture the complex relationships between microstructural features and mechanical responses. In particular, the project will
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 12 days ago
dynamics data and advanced graph-based deep learning models to decode long-range communication pathways within macromolecular complexes. The PhD candidate will play a central role in this effort by
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users, thanks to the use of machine learning tools and techno-economic analyses. This project is aligned with the sustainable development goals (SDG) 7 and 10 of the United Nations, by promoting a low
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | about 1 month ago
the communication and storage needed to retain most of the information. Environment. The PhD will take place at Inria Grenoble, in the Thoth team. This is a large team focused on machine learning, and
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 12 days ago
, are to be addressed. Objectives: The research of this PhD will be articulated around the concept of useful landmark for localization in complex environments. Indeed, unlike cases where object detection
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, machine learning techniques, etc.) is desirable. This thesis offer within the AstroParticle and Cosmology Laboratory (APC) is part of the Deep Underground Neutrino Experiment (DUNE). DUNE is an
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analysis, gravity models, Bayesian models, etc.). In this regard, proficiency in software is required: programming languages such as R or Python, machine learning, econometric softwares, data management
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stochastic modeling, Bayesian inference, data fusion and modern machine learning. Its research activities span various application domains such as security, non-destructive testing, infrared imaging and
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algorithms for asthma. The methods to be employed will include cell culture, transcriptomics, proteomics, multiplex assays, flow cytometry, and machine learning. This project combines expertise in cell biology
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macro- scales at IJL, and to train machine learning models to predict the microstructure evolution at larger scales and longer times at SIMAP lab and Laboratoire Analyse et Modélisation pour la Biologie