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Field
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digital twins be used to provide on-line predictions as to the future expected evolution of these critical properties as the basis for safe reinforcement learning (RL) for on-line optimal control”. In
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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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, Chemistry or related scientific fields and experience and knowledge managing and analyzing spectroscopic data to build predictive models. The Successful candidates should be able to work independently, have
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 month ago
to the weather prediction and climate projections. This is mainly due to our lack of understanding of cloud/snow ice microphysics and over-simplified representation in models. On a broader sense, although weather
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predictions of the models. Placement: La Rochelle Université, laboratory LaSIE, UMR 7356 CNRS Recruitment: fixed-term contract 12 months Working hours: full time Contract starting: Spring 2026 Salary: According
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understanding of the underlying physical mechanisms and to leverage this knowledge to develop predictive tools for optimizing the design and control of wind farms. Research scope and responsibilities Depending
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University Abu Dhabi invites applicants to apply for the open Post-doctoral Associate position to perform primarily research on Reinforcement Learning, and/or Optimal Control, and/or Model Predictive Control
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carbon materials by stereolithography, including experimental validation of predictive models and production of materials with controlled textural properties: a) Systematic bibliographic survey on 3D
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microfluidic analogs of phloem sieve plates and other plant hydraulic elements. Conduct controlled flow-pressure experiments to evaluate aspects of the theoretical predictions and quantify resistance mechanisms
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effectiveness and toxicity of the treatments. Other duties: Develop and validate cancer risk prediction models using deep neural networks based on semistructured data. Develop and validate learning strategies