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) department at Telecom Paris. Reinforcement learning (RL) has emerged as a useful paradigm for training agents to perform complex tasks. Model-based RL (MBRL), in particular, promises greater sample efficiency
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training an autonomous agent to ‘learn’ a control strategy. This formalism is similar to that of optimal control, with the difference that the agent does not have an explicit model of the dynamics
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researcher, with strong analytical and computational skills, with knowledge of one or all of the following: Dynamic Transport Modelling Demand Estimation and Forecasting Agent-based Modelling and Simulation
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Paris, is one of France's top 5 general engineering schools. The mainspring of Télécom Paris is to train, imagine and undertake to design digital models, technologies and solutions for a society and
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of knowledge. The analysed results will be used to build the agent-based simulator currently being developed by a PhD student. You will also be involved in meetings with stakeholders (elected officials, Grenoble
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in ecological fieldwork, ideally in wet grassland or agricultural systems - Skills in GIS, remote sensing, and spatial data analysis (bonus: agent-based modelling) - Demonstrated ability to work in
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from geomatics, spatial analysis, multi-agent modeling and simulation, and laboratory work (soil and surface formation studies; host of the Solsup platform of the EMerode service unit). General