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unsupervised learning Distributed / decentralised command and control: synchronisation, coordination, adaptation, for example using multi-agent systems Decision support under uncertainty Modelling and simulation
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modelling. MISSION You will actively contribute to the development and evaluation of new hybrid computational method to predict biological tissue deformation with subject-specific material properties
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to work on the use of hyperspectral data to explain and predict soil functions and communities in European mountains. We are looking for a candidate who has a very good command of artificial intelligence
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predict the location of resources more accurately, it is necessary to model these processes jointly at the basin scale. However, directly solving geochemical equations is computationally expensive and
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of the system to be controlled. This RL approach will be compared and contrasted with optimal control methods such as Model Predictive Control (MPC). The various control strategies developed will first be
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/Linux environment; - Good command of scientific English, both written and spoken (certified level). Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR7285-CARNOE-068/Default.aspx
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and sophisticated planning capabilities by enabling an agent to learn a predictive model of its environment. However, the direct application of current MBRL methods to safety-critical domains, such as
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phenomena at stake and the accurate prediction of such complex multi-phase, multi-physics system is then necessary to optimize the system parameters entirely. The first step consists in designing and
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predictive models. Leesu coordinates since 1994 the OPUR observatory of urban hydrology, a long-term research project and a means for structuring scientific expertise in the Paris metropolitan area. LEE (https