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Field
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the physical mechanisms controlling the suspension response and work in close collaboration with experimental researchers within the project, providing modeling insights and helping to interpret experimental
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the relationship between whole-tree water use and wood cell formation in trees, using advanced growth models to predict the future of wood formation and carbon allocation in forests facing drier conditions. Within
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. This may be extended to include potential flow theory based modelling as well. Develop deep learning surrogate models for fast prediction of motions, stresses, and loads Validate the deep learning model
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05.07.2026, Academic staff Our research combines mathematical modeling, numerical simulation, scientific computing, and data-driven methodologies to improve the predictive capabilities and
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knowledge of their development and structure. Working closely with colleagues at the Universities of Leeds and Reading, you will integrate theoretical understanding, observational data, and modelling
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Experimentation The department investigates the dynamics of the ocean carbon cycle, its variability and predictability, and its role in the Earth system through climate-carbon cycle feedbacks operating across
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assimilation system is a good advantage. Prior knowledge and experience in satellite observation, pre-processing, quality control, error modelling and correction are an advantage. Good programming experience is
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Computational Postdoctoral Fellow (Quantitative Modeling Group) - 106785 Division: BE-Biological Systems & Engineering Berkeley Lab’s (LBNL, https://www.lbl.gov/) Biological Systems and Engineering
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to develop predictive models for polymer-based materials. This project aims to leverage computational chemistry techniques and data-driven approaches to optimize the properties of novel polymer-based materials
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work closely with an interdisciplinary team spanning microbiology, engineering, and computation, and will contribute to developing predictive models that link bacterial physiology to infection outcome