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of dynamical systems, control of complex and uncertain systems, motion control for high-tech systems, model predictive, networked, supervisory, neuromorphic, and learning-based control, cyber-physical systems
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 2 months ago
. Preferred qualifications include experience with: Optimal Control and/or Model Predictive Control (MPC). Modeling of deformable parts. Real-time numerical optimization. LanguagesFRENCHLevelBasic
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. The successful candidate will combine extensive experimental testing with insulation lifetime modelling to study PD inception, degradation and endurance, and to develop approaches for predicting insulation
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to contribute to a more sustainable and better world? We, Wageningen Food & Biobased Research (WFBR), are looking for a researcher data & modelling. Are you the one who will strengthen our team? What will you do
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-models, thereby avoiding double counting of effects, • developing and evaluating temporal machine-learning models for sequence-to-sequence prediction of fuel behaviour, • extending surrogate models
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, link ecological and EO data, and integrate your findings into models to predict ecosystem functioning in response to global changes and management interventions. You will learn how to publish your
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-of-the-art statistical approaches. Actively communicate results to the developers of predictive models within ReDiLEEP. Disseminate your research through publications in leading international journals and
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to increase resource use efficiency. However, managing and optimizing these new cultivation systems requires crop models that can predict plant growth, crop yield and resource use under highly dynamic
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Engineers usually want predictability. This project embraces chaos! – Excited about control theory? Then join us to build the math of chaotic sampling for greener and more secure control systems
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greenhouses than constructing detailed CFD models for each location. Key expected innovations in this project are expected to include a hybrid data-driven and model-based predictive control approach that uses