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collaboration with other CEA teams, notably ; * Parallel and cluster computing environment and efficient LP/MILP algorithms for our large-scale models ; * Data structuring and storage solutions for model input
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environment: In the framework of PEPR Spin and more precisely of ADAGE project dedicated to the development of enhanced magnetic sensors, - main mission: The candidate will design, fabricate, and characterize
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | about 2 months ago
follows a phased algorithm: 1) generate an initial training set by uniformly sampling input points 2) (re)train the model on the trainng set 3) use feedback from the model’s performance to generate/augment
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(Carneiro et al., PLoS Genet 2016). Specifically, we identified cGAS–STING as a central sensor of telomere-associated DNA damage in vivo (Serifoglu et al. EMBO J 2025) telomere erosion in intestinal epithelia
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into refining computational strategies for large-scale molecular simulations in materials science and computational physics. The project will involve substantial numerical development, including algorithm design
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training, as well as on machine learning or generative AI models. Technology watch on AI recommendation models and optimization of recommendation algorithms. Implementation of the recommendation engine
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of Opportunity (SOP) for geolocation. The laboratory has developed algorithms to perform geolocation using such signals. We now wish to move on to an experimental phase. The mission will take place at the SAMOVAR
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compatible with in vivo imaging still needs to be developed. In practice, the first part of this project will involve familiarizing the student with algorithms for measuring cell motility in traditional FF-OCT
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-printed cochlear model to monitor mechanical behavior and final electrode positioning. Task 1.3 – Sensing capability Development of an integrated sensor to detect contact with cochlear walls. Integration
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to the IT development of cooperative applications for vehicular networks; Experimentation with different strategies when approaching a pedestrian crossing; use of sensors detecting stationary or moving