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on the assimilation of Distributed Acoustic Sensing (DAS) data, using fiber optic cables deployed for the internet to estimate the state or parameters of complex spatiotemporal dynamics in urban environments within
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agroecosystem model simulations. The successful candidate will play a key role in developing robust landscape-scale digital twins and advancing data assimilation techniques for agricultural and environmental
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Arts et Métiers Institute of Technology (ENSAM) | Paris 15, le de France | France | about 1 month ago
]. Reduced Order Modeling (ROM) and Data Assimilation (DA) are key tools for designing efficient monitoring solutions. In preparation for an upcoming ANR-funded project starting in 2026, we aim to develop a
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digital twins and advancing data assimilation techniques for agricultural and environmental applications. You will be part of a dynamic research team applying advanced remote sensing and simulation methods
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a process called data assimilation, to arrive at the best possible description of the evolution of smoke plumes. The data assimilation will, in particular, give us better estimates of the strength
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will build on recent advances in machine learning for dynamical systems to extract meaningful representations of complex flame dynamics, construct prognostic ROMs, and perform data assimilation
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for career development in environmental science. For further information on this project and details of how to apply to it please visit https://centa.ac.uk/studentship/2026-b15-a-multi-scale-quantitative
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' button above. Further information on how to apply for a CENTA studentship can be found on the CENTA website: https://centa.ac.uk/apply/ Please be aware that the successful applicant would be registered
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methods for data assimilation; and graph-based multi-scale neural network models. While the developed methods will be broadly applicable, particular emphasis will be put on the problem of inferring gas
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-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation methods for data assimilation; and graph-based multi