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new insights into the phenomena observed and enrich the databases required for deep learning methods. The neural networks currently being developed at LISTIC to detect and segment areas of movement in
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of the project. The net monthly salary is €2,200 The contract duration is 18 months Where to apply E-mail yassine.haddab@umontpellier.fr Requirements Research FieldEngineeringEducation LevelPhD or equivalent
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to cutting-edge facilities and interdisciplinary collaborations. Mentorship and career development support, including networking and grant-writing opportunities. How to Apply Please send a single PDF including
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 8 days ago
applied mathematics, control, or related fields, with knowledge in several of the following areas: Graph theory and network modeling Dynamical systems and physical modeling (ODE/PDE, multi-agent systems
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experimental validation of mathematical and computational models linking individual microscopic dynamics, information propagation, and collective structures (norms, social networks, global performance
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preparation of scientific publications related to the project. The postdoc will work in close collaboration with a network of bioinformaticians specializing in the design and development of data analysis
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of genetic networks Interplay between chromatin architecture and gene regulation Candidates will use a combination of skills in molecular and developmental biology, instrumentation and data analysis to answer
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. The work will be primarily computational, focusing on the development of deep neural network model architectures and their training. It will involve extending the preliminary results we have already obtained
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by identifying their targets and mechanisms of action. Using multi-omics data, the project will generate network models of the disease and the drugs to uncover regulatory mechanisms involved in both
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networks (GNNs) to accelerate therapeutic target identification. GenePPS aims to overcome current limitations of perturbation modelling by integrating large-scale single-cell foundation models with