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neural population dynamics recorded by experimental partners - Collaborate with project partners - Participate in scientific activities of the team and scientific consortium - Study learning mechanisms and
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quantitative and machine learning approaches ● Developing predictive models linking nuclear features to future cell fate ● Interacting with collaborators in imaging, computational biology, and developmental
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Description Within the ANR HEBBIAN contract, the objective is to adapt bio-inspired Hebbian learning models recently proposed by one of the partners of this ANR (Frédéric Lavigne) in order to account for data
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correlations. - Analyse experimental data mixing both quantum optics and solid-state physics phenomena - Supervise PhD students and interns - Ensure an efficient collaboration and information sharing with
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responsibility of developing predictive tools based on machine learning for the analysis and interpretation of Raman vibrational spectra applied to battery materials. The successful candidate will design and
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, statistical and case of study of switchbacks. Comparaisons with synthetic observations from the dedicated simulations of the JET2SB project. 2) Collaboration with the local LPC2E team, as well as the full
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collaboration with the project's principal investigator and partner laboratories, the postdoctoral researcher recruited at Praxiling will be responsible for the linguistic analysis and modeling of the multimodal
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project FBCUBES (France-Brazil Collaboration on Universal Behavior of Exotic Nuclear Systems) and contribute to one of its core work packages. The position focuses on developing and applying the No-Core
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: Marine Biodiversity and ecosystem functioning across spatial, temporal, and human scales”. The overall aim of the project is to acquire knowledge of the principles governing the structure, dynamics
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structures and corresponding images) needed for training and validating deep learning (DL) models. Work closely with members of the ICMN nanostructures group or external collaborators. Communicate research