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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 24 days ago
) algorithms for the identification of unknown BRP model parameters from time-course, single-cell (growth and gene expression) measurements over cell lineages. Develop methods for estimation of unobserved
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Institut of Mathematics of Marseille | Marseille, Provence Alpes Cote d Azur | France | about 2 months ago
nonstationary models and algorithms for analyzing various biological signals. The project will focus mainly on developing innovative models for biomedical signals with irregular cyclicity and exploring potential
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for: • Contributing to various tasks related to the modeling of lipids and membrane proteins involved in lipid droplet biogenesis. • Developing and implementing the POP-MD algorithm in the OpenMM software
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analysis, as many observed phenomena cannot be adequately modeled by stationary processes. The NOMOS project aims to develop a new generation of nonstationary models and algorithms for analyzing various
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teams to contribute to the development of fundamental aspects of computer science (models, languages, methods, algorithms) and to develop synergy between the conceptual, technological and societal
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optical medical devices. The works is aiming to develop optical devices and processing algorithms to guide neurosurgeons during the intraoperative neurofunctional assessment and connect these optical
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in the group optical medical devices. The works is aiming to develop optical devices and processing algorithms to guide surgeons during cancer surgery using fluorescence measurements. The post is based
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components: - operational modal analysis to extract the modes of the probed medium, - algorithmic and experimental developments on the MSE method - and algorithmic and experimental developments on the MFP
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evaluation of algorithms for: perception in robotics; sensor based control and navigation ; interactive mobile manipulation; multi-sensor data modelling and fusion. This job offer takes place within
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the necessary data from numerical models and observations to build the dataset; Identify the algorithms best suited to learn the targeted behaviors; Train the learning models; Validate their ability to predict