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: to understand the neurophysiological mechanisms and brain networks involved in neurological and psychiatric disorders (epilepsy, Parkinson's disease and obsessive-compulsive disorder), and to optimise
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forest vegetation dynamics in Québec across these same time scales. The project will rely on a network of regional study sites, including both short cores (ca. 2,000 years) and long cores (ca. 8,000
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analysis components and is part of a collaborative effort within the PEPR Cell-ID network. The successful candidate will be responsible for: ● Designing and implementing chromatin tracing experiments (Hi-M
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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 | 14 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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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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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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. 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