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on near real-time prediction of vector-borne disease risk, phylogeographic modeling of non-pathogenic viruses to estimate mosquito dispersal, and understanding how weather-dependent human behavior impacts
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or more of the following areas: Advanced Process Control and Optimization Digital Twin and Modeling & Simulation Predictive Maintenance and Fault Diagnosis Industrial IoT and Edge Computing Good programming
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developed using finite element analysis (FEA) between LMGC, ICube and LEM3 Labs to model the behaviour of Wharton's jelly samples in an ex vivo and in vivo context. Predictive tools, based on previous models
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Intelligent Control Systems RESPONSIBILITIES Develop industrial process digital twin models based on the fusion of mechanistic and data-driven approaches. Develop predictive maintenance and fault diagnosis
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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Control Section is to perform research and train next-generation students on the topic of understanding and predicting the dynamics of complex engineering systems in order to develop advanced control
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, effort, and experimental expenses, and to provide data that is unachievable through experiments. Chemical kinetic models form the basis for a predictive tool, used to understand, optimise, and engineer
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performance, and preventing failures like fires or explosions. Current prediction methods mainly rely on extensive lab testing and modeling, using insights from destructive post-mortem analyses to improve
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by the CNRS, the postdoctoral researcher will be responsible for contributing to the development of advanced methodologies for predicting crystal structures (CSP) based solely on their chemical
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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