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, “Generative diffusion models for atmospheric data assimilation” (f/m/d) The position is offered for three years with a start date as soon as possible. The salary is according to class EG 13 TV-L. The fixed-term
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Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The
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well as LiDAR measurements, into ensemble agroecosystem model simulations. The successful candidate will play a key role in developing robust landscape-scale digital twins and advancing data assimilation
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to the development of a comprehensive and robust model (data assimilation) of the Rhine Graben. The approach will be a hybrid data assimilation, physical model coupled with a neural network (PINNs). The use of soft
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boundary treatments. Integrating the surrogate models into a digital‑twin pipeline for real‑time data ingestion, assimilation, and visualisation. The project will deliver a real‑time digital‑twin
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of inversion or data-assimilation approaches that exploit the totality of data from both measurement and modelling. Third, the project will quantify uncertainty arising from flow variability, measurement noise
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. The Department of Molecular Biology/Umeå university, are searching for a DDLS PhD student in Data driven
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computational engineering and computer simulation data modeling and assimilation towards experimental measurements under consideration of uncertainties utilization of Explainable AI techniques to enable novel